{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import load_wine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林-分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "class sklearn.ensemble.RandomForestClassifier (n_estimators=’10’, criterion=’gini’, max_depth=None,\n",
    "min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’,\n",
    "max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False,\n",
    "n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine = load_wine()\n",
    "wine.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single Tree:0.8889 Random Forest:1.0000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)\n",
    " \n",
    "clf = DecisionTreeClassifier(random_state=0)\n",
    "rfc = RandomForestClassifier(random_state=0)\n",
    "clf = clf.fit(Xtrain,Ytrain)\n",
    "rfc = rfc.fit(Xtrain,Ytrain)\n",
    "score_c = clf.score(Xtest,Ytest)\n",
    "score_r = rfc.score(Xtest,Ytest)\n",
    " \n",
    "print(\"Single Tree:{:.4f}\".format(score_c)\n",
    "      ,\"Random Forest:{:.4f}\".format(score_r)\n",
    "     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest:0.961111111111111\n",
      "DecisionTree:0.8539215686274509\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "label = \"RandomForest\"\n",
    "# # n_estimators RF中基模型的数量 默认100个\n",
    "for model in [RandomForestClassifier(n_estimators=25),DecisionTreeClassifier()]:\n",
    "    #  交叉验证， 所有的结果作为列表返回\n",
    "    score = cross_val_score(model,wine.data,wine.target,cv=10)\n",
    "    \n",
    "    print(\"{}:{}\".format(label, score.mean()))\n",
    "    plt.plot(range(1,11), score, label = label)\n",
    "    plt.legend()\n",
    "    label = \"DecisionTree\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 随机森林是基于bagging思想的，主要关注降低方差，所以要求基学习器为强学习器保证偏差\n",
    "rfc_l = []\n",
    "clf_l = []\n",
    " \n",
    "for i in range(10):\n",
    "    rfc = RandomForestClassifier(n_estimators=25)\n",
    "    rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10).mean()\n",
    "    rfc_l.append(rfc_s)\n",
    "    \n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf_s = cross_val_score(clf,wine.data,wine.target,cv=10).mean()\n",
    "    clf_l.append(clf_s)\n",
    "    \n",
    "plt.plot(range(1,11),rfc_l,label = \"Random Forest\")\n",
    "plt.plot(range(1,11),clf_l,label = \"Decision Tree\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# 可以看到 RF比单颗树好，且这种好是稳定的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_score:0.9888888888888889, n_estimators:27\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 随着基学习器个数的增加，RF的性能变化\n",
    "superpa = []\n",
    "for i in range(100):\n",
    "    # n_jobs：指定使用的CPU核心数\n",
    "    rfc = RandomForestClassifier(n_estimators=i+1, n_jobs=-1)\n",
    "    rfc_s = cross_val_score(rfc,wine.data,wine.target,cv=10).mean()\n",
    "    superpa.append(rfc_s)\n",
    "    \n",
    "print(f\"max_score:{max(superpa)}, n_estimators:{superpa.index(max(superpa))+1}\")\n",
    "plt.figure(figsize=[20,5])\n",
    "plt.plot(range(1,101),superpa)\n",
    "plt.xlabel(\"n_estimators\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00036904803455582827"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.special import comb\n",
    "# 假设单棵树的错误率为0.15， 那么25棵树组成的随机森林输出错误结果的概率为：\n",
    "np.array([comb(25,i)*(0.2**i)*((1-0.2)**(25-i)) for i in range(13,26)]).sum()\n",
    "# 当然，实际上错误概率要更大一些，因为1.无法保证每棵树完全独立；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeClassifier(max_features='auto', random_state=1872583848)\n",
      "1872583848\n",
      "794921487\n",
      "111352301\n",
      "1853453896\n",
      "213298710\n",
      "1922988331\n",
      "1869695442\n",
      "2081981515\n",
      "1805465960\n",
      "1376693511\n",
      "1418777250\n",
      "663257521\n",
      "878959199\n",
      "854108747\n",
      "512264917\n",
      "515183663\n",
      "1287007039\n",
      "2083814687\n",
      "1146014426\n",
      "570104212\n"
     ]
    }
   ],
   "source": [
    "# random_state: 让RF里面所有决策树的random_state固定(并不是让每棵树的random_state相同)\n",
    "rfc = RandomForestClassifier(n_estimators=20,random_state=2)\n",
    "rfc = rfc.fit(Xtrain, Ytrain)\n",
    " \n",
    "#随机森林的重要属性之一：estimators，查看森林中树的状况\n",
    "print(rfc.estimators_[0])\n",
    " \n",
    "for i in range(len(rfc.estimators_)):\n",
    "    print(rfc.estimators_[i].random_state)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9831460674157303"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RF使用了bootstrap\n",
    "# oob_score设为True, 模型统计包外样本，并计算在包外样本上的准确率\n",
    "rfc = RandomForestClassifier(n_estimators=25,oob_score=True)#默认为False\n",
    "rfc = rfc.fit(wine.data,wine.target)\n",
    "\n",
    "# 查看“包外样本”上的准确率\n",
    "#重要属性oob_score_\n",
    "rfc.oob_score_#0.9719101123595506\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9814814814814815\n"
     ]
    }
   ],
   "source": [
    "#一些属性和接口\n",
    "rfc = RandomForestClassifier(n_estimators=25)\n",
    "rfc = rfc.fit(Xtrain, Ytrain)\n",
    "score = rfc.score(Xtest,Ytest)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13,)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.feature_importances_.shape # 查看特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 25)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.apply(Xtest).shape # apply返回每个测试样本所在的叶子节点的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 2, 0, 2, 0, 2, 1, 1, 2, 0, 0, 0, 2,\n",
       "       2, 0, 0, 0, 1, 0, 0, 2, 1, 1, 0, 0, 2, 2, 1, 1, 0, 1, 1, 1, 1, 2,\n",
       "       0, 0, 2, 0, 2, 0, 2, 2, 1, 0])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict(Xtest) # 接收样本，获取RF决策结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.predict_proba(Xtest).shape # 返回预测结果的概率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging一个必要条件： 要求基学器至少是一个弱学习器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1ahUAgwcP5ptvvmHUqFEMGjSIY8eOsXTpUtasWWOyGDUaTaaahgAaVyyWqey4ccVi2f5LbtasGUuXLuXMmTNYWFjg6+tL06ZNOXDgAPfv36dp06YG+z+dDKZ2sAP0fXe+++476tevb7Df0+vtPHme1KbB1OOfptPp8PLySrevQ+rQ9MmTJ/PGG2+wfft2fvvtNyZNmsTPP/9Mt27dnnMHDKUX17NiHTNmDLt27WLWrFlUqFABe3t7evToQVJS0jOvoyhKhk2lT3p6NJefnx+hoaH89ttv7N27l549e9KqVSs2bNiQ7nUCAgK4c+cO8+bNo3Tp0tja2tKgQYM08T19HZ1Ox5QpU/Q1cE+ys8u5KuLUWCwtLTl16lSav6Mnmxjt7e0N7mlmj3vW37QwjjETsSmKQnB4DHuDI/n9n9v8fSPa4HnvIva0rOJByyrujNnwN7djTFM7AdCumhcL3/RLMxrLM5tGq5ry/Hkq9jNnoGdPuHgRLC3hs88oNmYMnwXfNln8xjJrcnPy5EmaN2+u307tG9OvXz9WrFhBeHi4wTfTsmXLsmPHDkaOHMm3335L8eLFmT9/fq4ZBm7q7PtZUvvdzJs3j6ZNm6LRaGjatCkzZszg/v37DB8+PNPn8vDwoESJEly5coU+ffpkW4x+fn5ERERgZWVFmTJlMtyvUqVKVKpUiZEjR9K7d2+WL19Ot27dsLGxQavVZls8Tzp06BABAQH6JOrhw4dcvXrVYJ/0ru/r68vhw4cNyo4ePUqlSpWeu/Cii4sLvXr1olevXvTo0YN27doRFRWVbl+QQ4cOsWDBAjp06ADA9evXM+xE/yQ/Pz8uXLhAhQoVnrsvqE1/Xl5e/PnnnzRp0gRQa+xOnTqFn59fps6Rkdq1a6PVaomMjKRx48YmP+5ppvz7yU8yM1Q7MUXLn1ei2Bt8m99DbnPriX01GqjlXYhWPh608vGgkoeTPlmd3Nn074/tqnnR2tfTZLPkmvL8eSb2sWPVxKZkSfj5Z2jUKEfiN4ZZk5tmzZo985vVihUr0pSlN0okNzF19p2R1H43P/74I19//TWgJjyvvfYaycnJBv1tMmPy5MkMGzYMFxcX2rdvT2JiIidPnuT+/fsGHbSN0apVKxo0aEDXrl358ssvqVy5Mrdu3WLHjh107dqVqlWrMmbMGHr06EHZsmW5ceMGJ06c0CevZcqU4eHDh/z+++/UrFkTBweHbFvktEKFCmzatIlOnTqh0WiYMGFCmhqoMmXKcPDgQV5//XVsbW0pWrQoH374IXXr1mXatGn06tWLY8eO8c033xiMLEvP3Llz8fLyolatWlhYWLB+/Xo8PT0znFyxQoUK/PDDD9SpU4eYmBjGjBmjX8X6WSZOnMgrr7yCt7c3r732GhYWFvz999+cPXtWP5rqacOHD+eLL76gYsWK+Pj4MGfOnGyZgK9SpUr06dOHt956i9mzZ1O7dm3u3r3LH3/8QfXq1fWJW3Yd97QyZcqwa9cuLly4gJubG66uriZrzs6rnjdU+60GpYmMTeTgxTs8SnqcKNpZW9C4YjFa+3jQvIo7xZzT7+eYU++PlhYakw45NuX580Tsy5bB+PEwdy481c3A1PFnVp7qc5NXmCt7bd68OYGBgfpEpnDhwvj6+nLr1i18fHyMOtfbb7+Ng4MDM2fOZOzYsTg6OlK9evUXmsBOo9GwY8cOPvnkEwYMGMCdO3fw9PSkSZMmeHh4YGlpyb1793jrrbe4ffs2RYsW5dVXX2XKlCkANGzYkMGDB9OrVy/u3bvHpEmT9MPBX9TcuXMZMGAADRs2pGjRonz00Udp5kSaOnUq7777LuXLlycxMRFFUfDz82PdunVMnDiRadOm4eXlxdSpUw06E6fHycmJL7/8kn///RdLS0vq1q3Ljh07MpzPZdmyZbzzzjvUrl2bUqVK8fnnnxt0UM5I27Zt2bZtG1OnTuWrr77C2tqaKlWq8Pbbb2d4zIcffkh4eDgBAQFYWFgwYMAAunXrRnR0dIbHZNby5cuZPn06H374ITdv3sTNzY0GDRo8N0HJ6nFPGjRoEPv376dOnTo8fPiQffv2GZ3052fPG6oNsPLYNX2Zu7MtLX08aO3rTsPyRbHLZB/B3PTtXmTSqVOwZw+MG6dulygB/3UXya00SgFrlI6JicHV1ZXo6GhcXFwMnktISCA0NJSyZcvmeH8EIYT5FeT3gMwOF361dnECGpWlWnFXLCQhyd8UBb75BkaPhqQk2LIFOnUyWzjP+vx+mtTcCCGEyPQEa00ru1OjZCHTBiPM7/59GDgQNm9Wt7t2hZdfNmtIxshTMxQLIYQwjcxOsJaTE7EJM/nrL/DzUxMbGxuYPx82bYLChc0dWaZJzY0QQggcbS2x0EBGC3SbYyI2YQYLF8KwYZCSAuXKwbp14O9v7qiMJjU3QghRwO27EEnvJX8+M7GBnJ+ITZiBu7ua2Lz2GgQG5snEBiS5EUKIAu3HP6/x9sqTPErS0rC8G7N71sTL1bDpydPVLsMVu0U+8OjR45+7d4eDB2HtWnhqRvq8RJqlhBCiANLpFL7c+Q+LD14BoId/ST7vVh0bKwu61iohQ7ULAp0OvvpK7VNz8iT8t3YbLzBZZm4hyY0QQhQwCclaPlx3hu3/LWY5qnUlPmhRQT+TcG6ZiE2Y0J078NZbsHOnur1q1eN5bPIBSW6EEKIAufcwkUGrThIY9gBrSw1f9ahBt9olzR2WyEkHD0Lv3nDrFtjZqXPZDBhg7qiylfS5ySeaNWtmMHtwXFwc3bt3x8XFBY1Gk+Xp88uUKcO8efP02xqNhl9++eWFYjVWQEAAXbt2zdFrCpEfXbnzkG4LjhIY9gAXOyt+GFhfEpuCRKuF6dOheXM1sfHxgRMn1PlsNPmr2VFqbvKJTZs2GayTs3LlSg4dOsTRo0cpWrQornmgY9jVq1cpW7YsQUFB1KpVS1/+9ddf58jqzgEBATx48CDHkzchcsLx0Cje+eEkD+KS8S5iz/KAulRwdzZ3WCInzZsHEyaoP/frB99+C46OZg3JVCS5ySeeXkn68uXL+Pj4UK1aNTNFlH3yQmL2pKSkJGxsbPLNdUTe9+vpm4xZ/zdJWh01vQuxtF8dijqlv7ilyMcGD1ZHQb3/vprc5GPSLJVPPNks1axZM2bPns3BgwfRaDQZLg54+fJlunTpgoeHB05OTtStW5e9e/e+UByKovDVV19Rrlw57O3tqVmzJhs2bNA/f//+ffr06UOxYsWwt7enYsWKLF++HICyZcsCULt2bYO4n26WatasGR988AEjRoygcOHCeHh4sGTJEh49ekT//v1xdnamfPny/Pbbb/pjtFotAwcOpGzZstjb21O5cmX96umgroK+cuVKfv31VzQaDRqNhv379wNw9uxZWrRogb29PW5ubrzzzjs8fPhQf2xqfDNmzKB48eJUqlQJgAULFlCxYkXs7Ozw8PCgR48eGd63e/fu0bt3b0qWLImDgwPVq1dnzZo1Bvs0a9aMoUOHMmrUKIoWLUrr1q0BCA4OpkOHDjg5OeHh4UHfvn25e/duhte6du0anTp1onDhwjg6OlK1alV27NiRqfv05Ov9/PPP8fDwoFChQkyZMoWUlBTGjBlDkSJFKFmyJMuWLTM47ubNm/Tq1YvChQvj5uZGly5duHr1aoZxihenKArf7rvE8J9Pk6TV0baqBz8PekkSm4JCq1U7Cut06rajI/z5Z75PbEBqbjLvyXkAnmZpqXbKysy+FhZgb//8fV+gqnDTpk2MGzeOc+fOsWnTpgy/3T98+JAOHTowffp07OzsWLlyJZ06deLChQuUKlUqS9f+9NNP2bRpEwsXLqRixYocPHiQN998k2LFitG0aVMmTJhAcHAwv/32G0WLFuXSpUvEx8cDcPz4cerVq8fevXupWrXqM2slVq5cydixYzl+/Dhr165lyJAh/PLLL3Tr1o2PP/6YuXPn0rdvX8LCwnBwcECn01GyZEnWrVtH0aJFOXr0KO+88w5eXl707NmT0aNHExISQkxMjD7ZKlKkCHFxcbRr146XXnqJEydOEBkZydtvv83QoUNZsWKFPp7ff/8dFxcX9uzZg6IonDx5kmHDhvHDDz/QsGFDoqKiOHToUIavJyEhAX9/fz766CNcXFzYvn07ffv2pVy5ctSvX9/gdQ8ZMoQjR46gKArh4eE0bdqUQYMGMWfOHOLj4/noo4/o2bMnf/zxR7rXev/990lKSuLgwYM4OjoSHByMk5MTwHPvU6o//viDkiVLcvDgQY4cOcLAgQM5duwYTZo04a+//mLt2rUMHjyY1q1b4+3tTVxcHM2bN6dx48YcPHgQKysrpk+fTrt27fj777+lBsoEkrU6Pt18jrUnrwPw9stlGd/BR4Z0FxS3bsEbb8CBAxARAWPHquUWBaROQylgoqOjFUCJjo5O81x8fLwSHBysxMfHpz1QXR81/UeHDob7OjhkvG/Tpob7Fi2a/n5Gatq0qTJ8+HD99vDhw5WmT18rE3x9fZX//e9/+u3SpUsrc+fO1W8DyubNm9M99uHDh4qdnZ1y9OhRg/KBAwcqvXv3VhRFUTp16qT0798/3eNDQ0MVQAkKCjIo79evn9KlSxf9dtOmTZWXX35Zv52SkqI4Ojoqffv21ZeFh4crgHLs2LEMX+t7772ndO/ePcPrKIqiLFmyRClcuLDy8OFDfdn27dsVCwsLJSIiQn+ch4eHkpiYqN9n48aNiouLixITE5Ph9Z+nQ4cOyocffqjfbtq0qVKrVi2DfSZMmKC0adPGoOz69esKoFy4cCHd81avXl2ZPHlypuNI7z6VLl1a0Wq1+rLKlSsrjRs31m+n/k7WrFmjKIqiLF26VKlcubKi0+n0+yQmJir29vbKrl27Mh2LqT3zPSAPiY5PUt78/k+l9EfblLLjtikrj4aaOySRk3bufPzZ4uSkKKtXmzuibPGsz++nSc1NAfbo0SOmTJnCtm3buHXrFikpKcTHxxMWFpal8wUHB5OQkKBvLkmVlJRE7dq1ARgyZAjdu3cnMDCQNm3a0LVrVxo2bGj0tWrUqKH/2dLSEjc3N6pXr64v8/DwACAyMlJftmjRIr7//nuuXbtGfHw8SUlJBh2X0xMSEkLNmjVxfKImrVGjRuh0Oi5cuKC/TvXq1Q1qH1q3bk3p0qUpV64c7dq1o127dnTr1g0HB4d0r6PVavniiy9Yu3YtN2/eJDExkcTERIPrAtSpU8dg+9SpU+zbt09f8/Kky5cv65vInjRs2DCGDBnC7t27adWqFd27dze4n5m5T1WrVsXiiW+AHh4eBv27Un8nqff/1KlTXLp0CWdnww6sCQkJXL58Od17IrLm5oN4Biw/wYXbsdhbW/LNG7Vp6eNh7rBETkhJUTsMf/GFul2zpro2VDrvA/mdJDeZ9UQfizQsLQ23n/hATePpKkEz9jkYM2YMu3btYtasWVSoUAF7e3t69OhBUlJSls6n+69dd/v27ZQoUcLgOVtbtY2/ffv2XLt2je3bt7N3715atmzJ+++/z6xZs4y61pMjw0Adov5kWepkZKkxrVu3jpEjRzJ79mwaNGiAs7MzM2fO5K+//nrmdRRF0Z/raU+WP52EODs7ExgYyP79+9m9ezcTJ05k8uTJnDhxgkKFCqU51+zZs5k7dy7z5s2jevXqODo6MmLEiDS/i6evo9Pp6NSpE19++WWac3p5pT9V/ttvv03btm3Zvn07u3fvZsaMGcyePZsPPvgg0/fpefc/tSz1/ut0Ovz9/fnpp5/SxFOsWLF04xTPptUpaWYRDgmPYcCKE0TGJuLubMuygLpUK5G3OuSLLLpxQ5275vBhdXvIEJgzx7DLRAEiyU1mGdMHxlT7ZrNDhw4REBBAt27dALUPzot08PT19cXW1pawsDCaNm2a4X7FihUjICCAgIAAGjduzJgxY5g1a5a+5kOr1WY5howcOnSIhg0b8t577+nLnq4xsLGxSXNtX19fVq5cyaNHj/SJxZEjR7CwsEi3VuRJVlZWtGrVilatWjFp0iQKFSrEH3/8wauvvppufF26dOHNN98E1GTg33//xcfH55nX8PPzY+PGjZQpUwYrq8z/d/b29mbw4MEMHjyY8ePH89133/HBBx9k6j5lhZ+fH2vXrsXd3R0XF5cXPl9Bt/NcOFO2BhMenaAvK+xgzaMkLUkpOip7OLOsf11KFLJ/xllEvhIRAX/9BS4u8N138EQfuYKogPQsEumpUKECmzZt4vTp05w5c4Y33nhD/007K5ydnRk9ejQjR45k5cqVXL58maCgIL799ltWrlwJwMSJE/n111+5dOkS58+fZ9u2bfoPcHd3d+zt7dm5cye3b98mOjo6W14nqK/15MmT7Nq1i4sXLzJhwgROnDhhsE+ZMmX4+++/uXDhAnfv3iU5OZk+ffpgZ2dHv379OHfuHPv27eODDz6gb9+++iap9Gzbto358+dz+vRprl27xqpVq9DpdFSuXDnD+Pbs2cPRo0cJCQnh3XffJSIi4rmv6/333ycqKorevXtz/Phxrly5wu7duxkwYECGSeKIESPYtWsXoaGhBAYG8scff+h/B5m5T1nRp08fihYtSpcuXTh06BChoaEcOHCA4cOHc+PGjRc+f0Gy81w4Q34MNEhsAO7HJZOUoqOKpzPrhzSQxKYgeHL+rzp14Mcf1ZW8C3hiA5LcFGhz586lcOHCNGzYkE6dOtG2bVv8/Pxe6JzTpk1j4sSJzJgxAx8fH9q2bcvWrVv1w7xtbGwYP348NWrUoEmTJlhaWvLzzz8Dak3H/PnzWbx4McWLF6dLly4v/BpTDR48mFdffZVevXpRv3597t27Z1A7ATBo0CAqV65MnTp1KFasGEeOHMHBwYFdu3YRFRVF3bp16dGjBy1btuSbb7555vUKFSrEpk2baNGiBT4+PixatIg1a9ZQtWrVdPefMGECfn5+tG3blmbNmuHp6ZmpWZmLFy/OkSNH0Gq1tG3blmrVqjF8+HBcXV0N+sQ8SavV8v777+Pj40O7du2oXLkyCxYsyPR9ygoHBwcOHjxIqVKlePXVV/Hx8WHAgAHEx8dLTY4RtDqFKVuDedaUltHxyTjaSKV8vnf1qjrTcFDQ47KePaF8ebOFlJtoFCUHpn7NRWJiYnB1dSU6OjrNm2pCQgKhoaGULVsWuwLaTilEQZbb3wOOXb5H7+/+fO5+awa9JAtf5me//AL9+8ODB1C3rtoclc+WT0jPsz6/nyY1N0IIkUdExiY8fycj9hN5TFISjBgB3bqpiU39+upoqAKQ2BhLkhshhMgj3J0zV5uU2f1EHnLlCjRqBKkzhn/4obq6d5kyZg0rt5KGWSGEyCPqlS2Cl6tdms7EqTSAp6s6LFzkIyEh8NJLEBMDRYrAypXwyivmjipXk5obIYTIIywtNPRrWCbd51IbJiZ18pUlFvKbypXV5KZRIzh9WhKbTJCam3QUsD7WQoj/5Pb/+4qisDf4NgB21hYkJD+eusHT1Y5JnXxpVy39yRtFHnPpEhQvDg4O6uSva9eq86I9NVmmSJ8kN09InWE1Li4Oe3uZI0KIgiYuLg5IOwNzbrH173BOXruPvbUle0c1JSwqzmCGYqmxySd+/hkGDYJeveD779WydGY2FxmT5OYJlpaWFCpUSL8ejoODQ4ZT7wsh8g9FUYiLiyMyMpJChQph+fSSKrlAQrKWL3aEADCkWXlKFLanRGH5EpavxMfD8OHqDMMA//6rlsmXbaNJcvMUT09PwHDBRSFEwVCoUCH9e0Bus+TgFW5FJ1Dc1Y5BjcuZOxyR3UJC1En4zp1Th3Z/+ilMnAhGLKsiHpO79hSNRoOXlxfu7u4kJyebOxwhRA6xtrbOlTU2ABHRCSzcr67xNa6DD/Y2uTNOkUWrVqkLXcbFgYeHuoxCq1bmjipPk+QmA5aWlrn2jU4IUbB8ufMf4pO11CldmE41pMNwvnL/PowapSY2LVuqiU0urT3MSyS5EUKIXCww7D6bg24CMLGTr/QDzG8KF1Zrbk6dgo8/BvlSnS0kuRFCiFxKp1OYujUYgB7+JalRspB5AxIvTlFg2TIoWhRSFwfu0EF9iGwjyY0QQuRSW87c4vT1BzjYWDK2bWVzhyNeVGys2rfmp5/Uod3nz6tz2YhsJ8mNEELkQnFJKXzx2z8AvN+8Au4usl5UnnbmjDoa6uJFtenpo4+kb40JSXIjhBC50KIDV4iISaBkYXsGvlzW3OGIrFIUWLxYXc07MRFKloQ1a+Dll80dWb4myY0QQuQyNx/Es/iAOvT74w4+2FlLJ9M8KSUF+vSBdevU7Y4d1UUv3dzMG1cBIAtnCiFELvPFb/+QmKKjXtkitK8mTRd5lpWV2nHYygpmzYItWySxySFScyOEELnIyatRbD1zC40GJr4iQ7/zHEWBR4/AyUndnj0bBgwAf3/zxlXASM2NEELkEjqdwpT/hn73quNNtRKuZo5IGOX+fejeHTp3Bq1WLbOzk8TGDKTmRgghcolNQTc5ezMaJ1srPmwjQ7/zlOPH1VW8r14Fa2s4cQJeesncURVYUnMjhBC5wKPEFL7aqQ79HtqiAsWcbc0ckcgURYE5c6BRIzWxKVcOjh6VxMbMpOZGCCFygQX7LxEZm0hpNwf6Nypj7nBEZkRFQUAAbN2qbvfoAd9/D67SnGhuUnMjhBBmdj0qju8OhQLq0G9bKxn6nSe88Yaa2NjawoIF6pBvSWxyBam5EUIIM5vxWwhJKToalnejja+HucMRmTVzJkREwIoVUKuWuaMRT5CaGyGEMKM/r9xjx9kILDSy6neud+cObNr0eLt6dQgMlMQmF5LkRgghzET7xKrfveuVooqni5kjEhk6eFBNYnr1gj//fFxuIR+juZH8VoQQwkw2nLpOcHgMznZWjGpdydzhiPRotTB9OjRvDrduQYUKjyfoE7mW9LkRQggziE1IZuauCwAMb1kRNycZ+p3r3L6trg31++/q9ltvwbffSnKTB0hyI4QQZvDNvkvcfZhEuaKOvNWgjLnDEU/74w91NNTt2+DgoCY1AQHmjkpkkiQ3QgiRw67de8Tyw1cB+KSjDzZW0kMg1zl7Vk1sqlZVh3j7+po7ImEESW6EECKHfbY9hCStjsYVi9Kiiru5wxGpFAVSR6sNG6YuoxAQoNbciDxFvi4IIUQOOnrpLruDb2NpoZFVv3OT3buhSROIjVW3NRp47z1JbPIoSW6EECKHpGh1TN2mDv1+s34pKno4mzkiQUoKfPwxtG0Lhw/DF1+YOyKRDaRZSgghcsjak9f5JyIWV3trRrSSod9md+MG9O6tJjUAgwfDhAnmjUlkC0luhBAiB0THJzN790UARrSqSGFHGzNHVMBt3w79+sG9e+DsrC542bOnuaMS2cTszVILFiygbNmy2NnZ4e/vz6FDh565/08//UTNmjVxcHDAy8uL/v37c+/evRyKVgghsuZ/v/9L1KMkKrg78eZLpc0dTsG2bBm88oqa2Pj5QVCQJDb5jFmTm7Vr1zJixAg++eQTgoKCaNy4Me3btycsLCzd/Q8fPsxbb73FwIEDOX/+POvXr+fEiRO8/fbbORy5EEJk3pU7D1lx9CoAn3b0wdrS7N8rC7aOHcHLCz74AI4ehfLlzR2RyGYaRVEUc128fv36+Pn5sXDhQn2Zj48PXbt2ZcaMGWn2nzVrFgsXLuTy5cv6sv/973989dVXXL9+Pd1rJCYmkpiYqN+OiYnB29ub6OhoXFxkHRchhGlodQrHQ6OIjE1g5ZGrBF5/QPPKxVjev565QyuYTp82XOAyKgqKFDFXNCILYmJicHV1zdTnt9m+PiQlJXHq1CnatGljUN6mTRuOHj2a7jENGzbkxo0b7NixA0VRuH37Nhs2bKBjx44ZXmfGjBm4urrqH97e3tn6OoQQ4mk7z4Xz8pd/0Pu7Pxn+82kCrz8AoEmlYuYNrCBKSoIRI6B2bViz5nG5JDb5mtmSm7t376LVavHw8DAo9/DwICIiIt1jGjZsyE8//USvXr2wsbHB09OTQoUK8b///S/D64wfP57o6Gj9I6MaHiGEyA47z4Uz5MdAwqMT0jw3dWswO8+FmyGqAurKFWjUCL7+Wt0OCTFvPCLHmL3h9+kJrBRFyXBSq+DgYIYNG8bEiRM5deoUO3fuJDQ0lMGDB2d4fltbW1xcXAweQghhClqdwpStwTyrrX/K1mC0OrP1Big4Nm5Ua2tOnoTChWHLFpg61dxRiRxitqHgRYsWxdLSMk0tTWRkZJranFQzZsygUaNGjBkzBoAaNWrg6OhI48aNmT59Ol5eXiaPWwghMnI8NCrdGptUChAencDx0CgalHfLucAKkoQEGD1aXegSoGFDtTmqVCnzxiVylNlqbmxsbPD392fPnj0G5Xv27KFhw4bpHhMXF4eFhWHIlpaWgFrjI4QQ5hQZm3Fik5X9RBYcPfo4sRk7Fvbvl8SmADLrJH6jRo2ib9++1KlThwYNGrBkyRLCwsL0zUzjx4/n5s2brFq1CoBOnToxaNAgFi5cSNu2bQkPD2fEiBHUq1eP4sWLm/OlCCEE7s522bqfyIIWLWD6dHX+mvbtzR2NMBOzJje9evXi3r17TJ06lfDwcKpVq8aOHTsoXVqd4Co8PNxgzpuAgABiY2P55ptv+PDDDylUqBAtWrTgyy+/NNdLEEIIvXpli+Dlapdh05QG8HS1o15ZGamTbeLj1bWhRoyA/z47+OQTs4YkzM+s89yYgzHj5IUQwlg7z4Uz+MfANOWpwyQWvulHu2rSPzBb/POPOrPw2bPqqKhDh9TVvEW+lCfmuRFCiPyota8nhRys05R7utpJYpOdVq0Cf381sXF3h8mTJbERerJwphBCZKND/97hQVwyrvZW/K93be7HJePurDZFWVrIh+8Le/QIhg6FFSvU7RYt4Mcf1eUUhPiPJDdCCJGN1p+8AUC32iVpUsndzNHkM9euQYcOEBwMFhYwaZLav+a/UbNCpJLkRgghsknUoyR2B6tzd71Wp6SZo8mHPDzA2lqtpVm9Gpo1M3dEIpeS5EYIIbLJL0E3SdYqVCvhQtXiruYOJ394+BDs7dXaGTs72LQJnJzUfjZCZEA6FAshRDZQFIV1J9W163rWkQV6s8WZM2qn4enTH5eVKyeJjXguSW6EECIbnLsZwz8RsdhYWdC5pkwq+kIUBRYvhvr14eJFWLZM7UgsRCZJciOEENlg7Ul1wtG2VT0p5GBj5mjysJgY6N0bBg+GxES1A/GpU+DoaO7IRB4iyY0QQryghGQtv56+BUAvaZLKusBAddmEtWvBygpmzoStW6FoUXNHJvIY6VAshBAvaNf5CGITUihRyJ6Gstp31sTEqHPWREerC12uXQsvvWTuqEQeJTU3QgjxglI7EvfwL4mFTNSXNS4uak1Nly4QFCSJjXghktwIIcQLuB4Vx5FL99BoZG4box0/DidOPN5++23YvBmKyMKi4sVIciOEEC9g/Sl1RuJG5YtSsrCDmaPJIxQF5s6Fl1+G116D+/fVco1G1ocS2UL63AghRBZpdQob/muSklqbTIqKgoAAtaMwQJ066lIKQmQj+YsSQogsOnLpLreiE3Cxs6JtVU9zh5P7HT0KtWqpiY2NDXzzDaxfD64ym7PIXpLcCCFEFqV2JO5auwR21rJ4Y4Z0OvjqK2jSBK5fhwoV4M8/4f33pRlKmIQkN0IIkQUP4pLYff42IMstPJdGA0eOgFYLr7+uTspXu7a5oxL5mPS5EUKILPj19C2StDp8vFyoWtzF3OHkToryuJPw8uVqc9Rbb0ltjTA5qbkRQogsWHtCbZLqVackGvmwNqTTwWefQf/+aoID6vDufv0ksRE5QmpuhBDCSOduRhMcHoONpQVdapUwdzi5y+3b0Lcv7NmjbvfrB82bmzcmUeAYVXOTkpLClClTuH79uqniEUKIXG/9fx2JW1f1oLCjLJKp98cf6mioPXvA3l5dzbtZM3NHJQogo5IbKysrZs6ciVarNVU8QgiRqyUka/nlv0UypSPxf7RamDwZWrWCiAjw9YWTJ9VmKWmGEmZgdJ+bVq1asX//fhOEIoQQud/u4NtExydT3NWOlyvIatWA2gw1ZYrav2bAAHVJBV9fc0clCjCj+9y0b9+e8ePHc+7cOfz9/XF0dDR4vnPnztkWnBBC5Dbrn1gk01IWyVQNHAjbt8O338Kbb5o7GiHQKEpqV/bMsXjGNNkajSbXN1nFxMTg6upKdHQ0Li4yfFMIkXk37sfR+Kt9KAocHNOcUm4FdC2plBQ4fx5q1nxcdv8+FC5svphEvmfM57fRzVI6nS7DR25PbIQQ4kVsOHUDRYEG5dwKbmJz4wa0aAGNG8OlS4/LJbERuYjMcyOEEJmg0ymsP6muAN6rbgHtSLxjhzoa6tAhdfvJ5EaIXCRLyc2BAwfo1KkTFSpUoGLFinTu3JlDqX/sQgiRDx27co+bD+JxtrOiXbUCtkhmcjKMHQsdO8K9e+DnB4GB0K6duSMTIl1GJzc//vgjrVq1wsHBgWHDhjF06FDs7e1p2bIlq1evNkWMQghhdqmLZHauWbxgLZIZFgZNm8LMmer20KHq6t4VKpg3LiGewegOxT4+PrzzzjuMHDnSoHzOnDl89913hISEZGuA2U06FAshjBUdl0zdz/eSlKJjy9BG1ChZyNwh5ZxPP1WXUnB1haVLoXt3c0ckCiiTdii+cuUKnTp1SlPeuXNnQkNDjT2dEELkelvO3CQpRUcVT2eql3A1dzg5a+JEePddCAqSxEbkGUYnN97e3vz+++9pyn///Xe8vQtoJzshRL627r+OxK/V8c7/i2SGhsKQIWo/GwAbG1i0CMqWNW9cQhjB6En8PvzwQ4YNG8bp06dp2LAhGo2Gw4cPs2LFCr7++mtTxCiEEGYTfCuGszejsbbU0K12Pl8kc+NGdUK+6Ghwd1dnHRYiDzI6uRkyZAienp7Mnj2bdevWAWo/nLVr19KlS5dsD1AIIcwptSNxa18PiuTXRTITEmD0aHWGYYAGDdQkR4g8yqjkJiUlhc8++4wBAwZw+PBhU8UkhBC5QmKKll9O3wTUJql86dIl6NlT7VMD6pDv6dPB2tq8cQnxAmRVcCGEyMDe4EgexCXj6WJHk4rFzB1O9tuxQ52zJigI3NzU9aG+/FISG5HnyargQgiRgXX5fZHM8uVBp1OXUjh9Gjp0MHdEQmQLWRVcCCHScetBPAf/vQOoyU2+8eABFCqk/ly5srqUQvXqYGX0x4EQuVaWOhSDOmnf0/LCquBCCJEZG/9bJLN+2SKUKer4/APygh9/hPffhy1b1FmHAWrXNm9MQpiArAouhBBP0ekU1p9S57bpmR86EsfFwYAB0LcvxMTAkiXmjkgIkzIquUlJScHKyopz586ZKh4hhDC7P0PvERYVh5OtFR2qe5k7nBdz/jzUrQvLl4NGA5Mnw6pV5o5KCJMyqlnKysqK0qVLSw2NECJfW//fjMSdahbH3iaPLpKpKLBihdoMFR8Pnp6wejU0b27uyIQwOaObpT799FPGjx9PVFSUKeIRQgiziklIZsfZcAB61snDHYn37VObouLjoXVrOHNGEhtRYBjdoXj+/PlcunSJ4sWLU7p06TSjpQIDA7MtOCGEyGlbz9wiMUVHRXcnankXMnc4Wde8OfTpA76+MG4cWBj9XVaIPMvo5KZr164mCEMIIXKHdSfUuW161c1ji2QqCvzwA3TqBIULq/1rfvhB/VeIAsbo5GbSpEmmiEMIIczun4gYztyIxspCQ9e8tEhmTAy8+y78/DN066YugKnRSGIjCqws1VM+ePCA77//3qDvTWBgIDdv3szW4IQQIieldiRu6eNOUSdbM0eTSYGB4O+vJjaWluqil4pi7qiEMCuja27+/vtvWrVqhaurK1evXmXQoEEUKVKEzZs3c+3aNVbJEEMhRB6UlKJjc5D6Ba1X3Twwt42iqKt4f/ghJCVBqVJqgtOggbkjE8LsjK65GTVqFAEBAfz777/Y2dnpy9u3b8/BgwezNTghhMgpv4fcJupREu7Otrl/kcwHD+C11+CDD9TEpnNndfFLSWyEALKQ3Jw4cYJ33303TXmJEiWIiIjIlqCEECKnpS6S2d2/JFaWuXxkkVYLx4+rq3fPmwe//AJFipg7KiFyDaObpezs7IiJiUlTfuHCBYoVy+XfdoQQIh0R0QkcuKgukvlabl0kM7UfjUYDbm6wfr06vLtuXfPGJUQuZPTXky5dujB16lSSk5MBdbHMsLAwxo0bR/fu3bM9QCGEMLWNgTfQKVCvTBHKFXMydzhpRUVB167qEgqp6teXxEaIDBid3MyaNYs7d+7g7u5OfHw8TZs2pUKFCjg7O/PZZ5+ZIkYhhDAZRVH0TVKv5cYZiY8dU1fu3rJF7TycTs25EMKQ0c1SLi4uHD58mD/++IPAwEB0Oh1+fn60atXKFPEJIYRJaHUKx0OjOHb5LtfuxeFgbZG7FsnU6WD2bPj4Y0hJgfLlYd06cHExd2RC5HpGJzepWrRoQYsWLbIzFiGEyBE7z4UzZWsw4dEJ+jIFDYf+vUO7arkgwbl7F/r1gx071O1evWDJEklshMikXD4kQAghstfOc+EM+THQILEBiE/WMuTHQHaeCzdTZP95+FCdlG/HDrC1hcWLYc0aSWyEMILZk5sFCxZQtmxZ7Ozs8Pf359ChQ8/cPzExkU8++YTSpUtja2tL+fLlWbZsWQ5FK4TIy7Q6hSlbg3nW/L1Ttgaj1Zlxhl8nJ7XWpnJldbj3O+/IMgpCGCnLzVLZYe3atYwYMYIFCxbQqFEjFi9eTPv27QkODqZUqVLpHtOzZ09u377N0qVLqVChApGRkaSkpORw5EKIvOh4aFSaGpsnKUB4dALHQ6NoUN4t5wKLjIS4OChTRt2eOBHGjlUTHSGE0cya3MyZM4eBAwfy9ttvAzBv3jx27drFwoULmTFjRpr9d+7cyYEDB7hy5QpF/puwqkzqm0EGEhMTSUxM1G+nN0ePEKJgiIzNOLHJyn7ZYt8+eOMNKF4cjh5Vm6KsrCSxEeIFGN0sFRgYyNmzZ/Xbv/76K127duXjjz8mKSkp0+dJSkri1KlTtGnTxqC8TZs2HD16NN1jtmzZQp06dfjqq68oUaIElSpVYvTo0cTHx2d4nRkzZuDq6qp/eHvngTVjhBAm4e5s9/ydjNjvhWi1MGUKtGoFERGQkKDW4AghXpjRyc27777LxYsXAbhy5Qqvv/46Dg4OrF+/nrFjx2b6PHfv3kWr1eLh4WFQ7uHhkeEyDleuXOHw4cOcO3eOzZs3M2/ePDZs2MD777+f4XXGjx9PdHS0/nH9+vVMxyiEyF/qlS2Cl6sdGfVg0QBernbUK2vipQzCw6FNG5g8WR3y3b+/2r9GvnwJkS2MTm4uXrxIrVq1AFi/fj1NmjRh9erVrFixgo0bNxodgOapjnKKoqQpS6XT6dBoNPz000/Uq1ePDh06MGfOHFasWJFh7Y2trS0uLi4GDyFEwWRpoWFSJ990OxSnvutM6uSLpYUJO/Du2QO1asEff4CjI6xaBcuWqT8LIbKF0cmNoijodDoA9u7dS4cOHQDw9vbm7t27mT5P0aJFsbS0TFNLExkZmaY2J5WXlxclSpTA1dVVX+bj44OiKNy4ccPYlyKEKIDaVfPiVb8Saco9Xe1Y+Kafaee5URS1s3BkJFSvDidPQt++prueEAWU0R2K69Spw/Tp02nVqhUHDhxg4cKFAISGhmaYlKTHxsYGf39/9uzZQ7du3fTle/bsoUuXLuke06hRI9avX8/Dhw9x+q+z3cWLF7GwsKBkyVw4bboQIle6HPkQgAGNylDTuxDuzmpTlElrbEAd0r16NXz9NcyYAfb2pr2eEAWU0TU38+bNIzAwkKFDh/LJJ59QoUIFADZs2EDDhg2NOteoUaP4/vvvWbZsGSEhIYwcOZKwsDAGDx4MqP1l3nrrLf3+b7zxBm5ubvTv35/g4GAOHjzImDFjGDBgAPbyJiGEyISwe3GcuRGNhQaGNKtAl1olaFDezXSJzc6d8MUXj7fLloV58ySxEcKEjK65qVGjhsFoqVQzZ87E0tLSqHP16tWLe/fuMXXqVMLDw6lWrRo7duygdOnSAISHhxMWFqbf38nJiT179vDBBx9Qp04d3Nzc6NmzJ9OnTzf2ZQghCqjtZ9UZiBuUd6OYs63pLpScDBMmwJdfqtsNGkDTpqa7nhBCT6Moihmn4sx5MTExuLq6Eh0dLZ2LhSiAXvnfIc7djOHzbtV5o376k4W+sLAw6N1bnbcG4P33YdYssMuBIeZC5FPGfH5nuuambNmyBqOYrly5kvUIhRDCDK7efcS5mzFYWmhoWzXzfQSNsmULBATA/fvg6gpLl0L37qa5lhAiXZlOblasWGHCMIQQwvRSm6QalnfDzckETVKffgqffab+XLcu/PwzlCuX/dcRQjxTppObptJWLITI47b9rSY3r9Qw0XDvypXVf0eMUPva2NiY5jpCiGcyerSUpaUlkelMEX7v3j2jOxQLIUROuXznISHhMVhZaGjj65l9J75///HPffvCqVMwd64kNkKYUZYm8UtPYmIiNvKfWQiRS+34r9amUYWiFHbMhveqxET44AN1Mr47dx6X+/m9+LmFEC8k081S8+fPB9TlEr7//nv9JHoAWq2WgwcPUqVKleyPUAghskFqf5uO2dEkdekS9OoFgYH/nXy72olYCJErZDq5mTt3LqDW3CxatMigCcrGxoYyZcqwaNGi7I9QCCFe0KXIWP6JiMXaUkPbF22SWrcO3n4bYmPBzQ1WroSOHbMnUCFEtsh0chMaGgpA8+bN2bRpE4ULFzZZUEIIkZ1SOxI3rlgMVwfrrJ0kPh5GjoTFi9Xtl1+GNWtAln4RItcxus/Nvn37DBIbrVbL6dOnuf9kpzohhMhFtv+X3HSs/gJNUlOnqomNRgMffwz79kliI0QuZXRyM2LECJYuXQqoiU2TJk3w8/PD29ub/fv3Z3d8QgjxQi7ejuXfyIfYWFrQyvcFJu4bN05dQmHnTnUuGyujV68RQuQQo5Ob9evXU7NmTQC2bt3K1atX+eeffxgxYgSffPJJtgcohBAvIrVJqkmlorjaG9EkFRcHCxdC6ghRV1c4cgTatDFBlEKI7GR0cnPv3j08PdUOeTt27OC1116jUqVKDBw4MN0FNYUQwlwURWHb37cAI0dJBQdDvXrw3nuwYMHjco2JVg4XQmQro5MbDw8PgoOD0Wq17Ny5k1atWgEQFxcnk/gJIXKVfyJiuXLnETZWFrTyyWST1IoV6tIJ58+Dpyf4+Jg0RiFE9jO60bh///707NkTLy8vNBoNrVu3BuCvv/6SeW6EELlKakfiZpWK4Wz3nCaphw/V1btXrVK3W7WCH38EDxMtsCmEMBmjk5vJkydTrVo1rl+/zmuvvYatrbr4nKWlJePGjcv2AIUQIisURcn8xH1nz0LPnvDPP2BhoY6MGj9e/VkIkedkqbt/jx49AEhISNCX9evXL3siEkKIbBAcHkPo3UfYWlnQ8nlNUtHR8O+/ULy4OndNkyY5E6QQwiSM/lqi1WqZNm0aJUqUwMnJiStXrgAwYcIE/RBxIYQwt9RRUi2quONkm873uCfXyXv5Zfj5Zzh9WhIbIfIBo5Obzz77jBUrVvDVV18ZLJRZvXp1vv/++2wNTgghskJRlMcT96XXJBUUpC5wGRz8uKxHDyhWLIciFEKYktHJzapVq1iyZAl9+vQxGB1Vo0YN/vnnn2wNTgghsuLczRjCouKws7agRRX3x08oijq0+6WX1FqaDz80W4xCCNMxus/NzZs3qVChQppynU5HcnJytgQlhBAvYttZdW6bllU8cLD5720uOlpd8HLDBnW7UydYvtxMEQohTMnompuqVaty6NChNOXr16+ndu3a2RKUEEJkVbpNUidPQu3aamJjbQ1z5sCvv6qregsh8h2ja24mTZpE3759uXnzJjqdjk2bNnHhwgVWrVrFtm3bTBGjEEJk2pkb0dy4H4+DjSXNK7vDsWPQtCkkJ0OZMrB2rTr7sBAi3zK65qZTp06sXbuWHTt2oNFomDhxIiEhIWzdulU/oZ8QQpjL9v+WW2jp44G9jaU62/BLL8Grr6odiSWxESLfM6rmJiUlhc8++4wBAwZw4MABU8UkhBBZktokVTXiEq/0rKYWWlnB9u3g5CRrQwlRQBhVc2NlZcXMmTPRarWmikcIIbIs6FoUHXev5pcfPqTl8tmPn3B2lsRGiALE6GapVq1asX//fhOEIoQQL+DuXZxe684n+5dhrdNidScS5IuYEAWS0R2K27dvz/jx4zl37hz+/v44OjoaPN+5c+dsC04IITLl8GGU11+n0s2bJFpac/nTz/CdNFpqa4QooDSK8uQc5M9n8YyF5DQaTa5vsoqJicHV1ZXo6GhcXFzMHY4Q4kXodPDllzBhAmi1XC5SgrE9PuGnb97Bztry+ccLIfIMYz6/ja650el0WQ5MCCGy1a1b8MUXoNXyd9OO9K7dj7YvVZTERogCLkurggshRK5QsiSsWIHuXhRvR5Ti0cOk9NeSEkIUKEZ3KAY4cOAAnTp1okKFClSsWJHOnTunO2uxEEJkK60Wpk2DXbsel3XrxokWXYl8mISznRWNK8ril0IUdEYnNz/++COtWrXCwcGBYcOGMXToUOzt7WnZsiWrV682RYxCCAEREdCmDUycCG++Cffv65/aflZdbqFtVU9srLL0nU0IkY8Y3Sz12Wef8dVXXzFy5Eh92fDhw5kzZw7Tpk3jjTfeyNYAhRCCvXvVhOb2bXB0VNeGKlwYAK1OYcfZCABpkhJCAFmoubly5QqdOnVKU965c2dCQ0OzJSghhAAgJUUdCdWmjZrYVK+uLoLZt69+l+OhUdx9mIirvTWNyhc1Y7BCiNzC6OTG29ub33//PU3577//jre3d7YEJYQQxMVBy5YwfTooCrzzDvz1F1SpYrDb9rPqWlJtq3pIk5QQAshCs9SHH37IsGHDOH36NA0bNkSj0XD48GFWrFjB119/bYoYhRAFkYMDlC0LgYHw3Xfw+utpdknR6vjtvyapV2oUz+kIhRC5lNHJzZAhQ/D09GT27NmsW7cOAB8fH9auXUuXLl2yPUAhRAGSnKzW2Li6qtvffguffgoVKqS7+1+hUdx7lERhB2salHfLwUCFELlZlua56datG926dcvuWIQQBdn162rtjKsrbNsGFhZq5+EMEhuAbX+ro6TaVfPE2lKapIQQqky/G5QqVYp79+7pt7/55htiYmJMEpQQooDZuhVq1YKjR+HIEbh48bmHpGh17DynJjcdq0uTlBDisUwnNzdu3DBYN+rjjz/m7t27JglKCFFAJCXBhx9C584QFQV16kBQUJpOw+k5evke9+OScXO04aVyRXIgWCFEXpHl5ReMXG9TCCEMXb0KvXrB8ePq9ogR6jpRtraZOnz7E01SVtIkJYR4gqwtJYTIeYoCPXrAqVNQqBCsWAFGDEhI1urYeV4m7hNCpM+o5Ob777/HyckJgJSUFFasWEHRooaTZg0bNiz7ohNC5E8aDSxapDZJrVoFpUsbdfiRS3eJjk+mqJMN9cvKKCkhhCGNksn2pTJlyqDRaJ59Mo2GK1euZEtgphITE4OrqyvR0dG4uLiYOxwhCo7Ll9X+ND16PC5TFDXRMdKY9WdYf+oGfV8qzbSu1bIxSCFEbmXM53ema26uXr36onEJIQqq9evh7bchIQHKl4fatdXyLCQ2SSk6dp1PnbhPmqSEEGlJLzwhhOkkJMB770HPnhATA/XqQbFiL3TKw5fuEJOQgruzLXXKyCgpIURaWepQfPz4cfbv309kZCQ6nc7guTlz5mRLYEKIPO7iRTWpOXNGraEZPx6mTAGrFxvHkDpxX4fqXlhaGF/zI4TI/4x+l/n888/59NNPqVy5Mh4eHgb9cJ7XJ0cIUUCsXq0udPnokVpT8+OP6sreLygxRcue87cBGSUlhMiY0cnN119/zbJlywgICDBBOEKIfOHqVTWxadYMfvoJimfPDMKHLt4lNjEFTxc7/EsVzpZzCiHyH6OTGwsLCxo1amSKWIQQeZlOp64HBTBunJrQ9O0LlpbZdoltf98C1CYpC2mSEkJkwOgOxSNHjuTbb781RSxCiLxq5Upo2FBd0RvUJCcgIFsTm4RkLXuCpUlKCPF8RtfcjB49mo4dO1K+fHl8fX2xtrY2eH7Tpk3ZFpwQIpd79Ajef19NbgAWL4aRI01yqQMX7/AoSUtxVztqexcyyTWEEPmD0cnNBx98wL59+2jevDlubm7SiViIgursWXU01D//qDU1U6eCCWco3/7EKClpkhJCPIvRyc2qVavYuHEjHTt2NEU8QojcTlFg6VL44AN1HpvixWHNGmjSxGSXjE/SsjdEbZJ6pWb2dE4WQuRfRve5KVKkCOXLlzdFLEKIvOCLL2DQIDWxadcOTp82aWIDsP9CJHFJWkoUsqdmSVeTXksIkfcZndxMnjyZSZMmEZfacVAIUbD07QuenmqSs337C884nBnbzqpNUq/U8JKmcCHEcxmd3MyfP5/ffvsNDw8Pqlevjp+fn8HDWAsWLKBs2bLY2dnh7+/PoUOHMnXckSNHsLKyolatWkZfUwhhBEWBI0ceb5csCf/+Cx999Hjot4lodQr7L0Sy+7+1pNpV8zTp9YQQ+YPRfW66du2abRdfu3YtI0aMYMGCBTRq1IjFixfTvn17goODKVWqVIbHRUdH89Zbb9GyZUtu376dbfEIIZ4SHa0ueLlhA/zyC3TpopY7OZn80jvPhTNlazDh0Qn6siE/BTK5ky/tqslQcCFExjSKoijmunj9+vXx8/Nj4cKF+jIfHx+6du3KjBkzMjzu9ddfp2LFilhaWvLLL79w+vTpTF/TmCXThSjQTp6EXr3gyhWwtob582Hw4By59M5z4Qz5MZCn35xSG6QWvuknCY4QBYwxn99ZrlM+deoUP/74Iz/99BNBQUFGH5+UlMSpU6do89R6M23atOHo0aMZHrd8+XIuX77MpEmTMnWdxMREYmJiDB5CiGdQFPj6a3VSvitXoEwZOHw4xxIbrU5hytbgNIkNoC+bsjUYrc5s38uEELmc0c1SkZGRvP766+zfv59ChQqhKArR0dE0b96cn3/+mWKZ7Fx49+5dtFotHh4eBuUeHh5ERESke8y///7LuHHjOHToEFaZXFl4xowZTJkyJVP7ClHg3b8PAwaoTVAAr76qDvsuVCjHQjgeGmXQFPU0BQiPTuB4aBQNyrvlWFxCiLzD6JqbDz74gJiYGM6fP09UVBT379/n3LlzxMTEMCwLE3g9PfJBUZR0R0NotVreeOMNpkyZQqVKlTJ9/vHjxxMdHa1/XL9+3egYhSgwDh5UExsbG/jf/9S+NjmY2ABExmac2GRlPyFEwWN0zc3OnTvZu3cvPj4++jJfX1++/fbbNE1Mz1K0aFEsLS3T1NJERkamqc0BiI2N5eTJkwQFBTF06FAAdDodiqJgZWXF7t27adGiRZrjbG1tsbW1zXRcQhRoXbrA9Onq/DX+/mYJwd3ZLlv3E0IUPEbX3Oh0ujTrSQFYW1uj0+kyfR4bGxv8/f3Zs2ePQfmePXto2LBhmv1dXFw4e/Ysp0+f1j8GDx5M5cqVOX36NPXr1zf2pQgh7t1TF7gMD39c9sknZktsAOqVLYKXa8aJiwbwcrWjXtkiOReUECJPMbrmpkWLFgwfPpw1a9ZQvLg6DfrNmzcZOXIkLVu2NOpco0aNom/fvtSpU4cGDRqwZMkSwsLCGPxfx8Xx48dz8+ZNVq1ahYWFBdWqVTM43t3dHTs7uzTlQohMOHIEXn8dbtyAyEjYscPcEQFgaaFhQkdf3lsdmOa51AbrSZ18sZT1pYQQGTA6ufnmm2/o0qULZcqUwdvbG41GQ1hYGNWrV+fHH3806ly9evXi3r17TJ06lfDwcKpVq8aOHTsoXbo0AOHh4YSFhRkbohDiWXQ6+Oor+PRT0GqhUiV4xtQL5lDY0QZQk5knx0R5utoxSea5EUI8R5bnudmzZw///PMPiqLg6+tLq1atsjs2k5B5bkSBducOvPUW7NypbvfpAwsXgrOzeeN6yqi1p9kUdJNedb3pWqsEkbEJuDurTVFSYyNEwWTM57fRNTepWrduTevWrbN6uBAip507B23bwq1bYG8P33wD/ftDLlurKSYhmR3n1D5Ar9f1pnapwmaOSAiR12QquZk/f36mT5iV4eBCiBxQpgy4uICrK6xbB7m0r9rWM7dISNZR0d2JWt6FzB2OECIPylRyM3fuXIPtO3fuEBcXR6H/5r948OABDg4OuLu7S3IjRG5y7x4ULqwucOnkpHYadncHR0dzR5ahdSdvANCzjresAC6EyJJMDQUPDQ3VPz777DNq1apFSEgIUVFRREVFERISgp+fH9OmTTN1vEKIzPr9d6haFebMeVxWtmyuTmwuRMRy5voDrCw0dPMrYe5whBB5lNHz3EyYMIH//e9/VK5cWV9WuXJl5s6dy6effpqtwQkhskCrhYkToXVruH0bVq+GlBRzR5Up60+qM4i39HGnqJNMvimEyBqjk5vw8HCSk5PTlGu1Wm7fvp0tQQkhsujWLWjZEqZNUxfAHDRInc8mk2uxmVNSio5NQTcBtUlKCCGyyujkpmXLlgwaNIiTJ0+SOor85MmTvPvuu3lmOLgQ+dKuXVCzJhw4oPavWb0alixRR0blAX/8c5uoR0m4O9vStFLmFuAVQoj0GJ3cLFu2jBIlSlCvXj3s7OywtbWlfv36eHl58f3335siRiHE84SHq+tC3b0LtWrBqVPQu7e5ozJKakfi7v4lsbI0+q1JCCH0jK6rLlasGDt27ODixYv6Sfx8fHyMWqlbCJHNvLzgyy/h4kWYPRvs8taikrdjEth/IRKA1/xLmjkaIURel+WG+EqVKklCI4Q5bd8OJUqoNTUAw4ebNZwXseHUDXQK1C1TmHLFnMwdjhAijzM6udFqtaxYsYLff/+dyMjINCuB//HHH9kWnBAiHUlJ8PHHag1NxYpqE1QuWz7BGIqi6EdJSUdiIUR2MDq5GT58OCtWrKBjx45Uq1ZNJtkSIiddvaqu5P3XX+p2x45gY2PWkF7Uiav3uXovDkcbSzpUlwUxhRAvzujk5ueff2bdunV06NDBFPEIITLyyy/qWlAPHkChQrBihdqJOI9be0KttXmlRnEcbXP/kHUhRO5n9JAEGxsbKlSoYIpYhBDpSU5W+9N066YmNi+9BKdP54vEJjYhmR1n1UUye9aVjsRCiOxhdHLz4Ycf8vXXX+vnuBFCmJiFBQQHqz+PHg0HD0Lp0uaNKZts/zuc+GQt5Ys54ierfwshsonRdcCHDx9m3759/Pbbb1StWhVra2uD5zdt2pRtwQlRoOl0amJjaQk//qh2HM5nzcHrnuhILP33hBDZxejkplChQnTr1s0UsQghABISYNQodY2oxYvVMg+PfJfYXIqMJTDsAZaySKYQIpsZndwsX77cFHEIIUCdhK9nTzhzRt1+/32oUcO8MZlI6ozEzSu74+6ctyYdFELkbjLHuRC5xerV4O+vJjbFisHOnfk2sUnW6tgUqCY3verK3DZCiOyVpXGXGzZsYN26dYSFhZGUlGTwXGBgYLYEJkSBERenjoZKXZutaVM10Sle3LxxmdAf/0Ry92ESRZ1saVZZFskUQmQvo2tu5s+fT//+/XF3dycoKIh69erh5ubGlStXaN++vSliFCL/UhR1Ir7vvweNBiZMgL1783ViA+hnJO7uVwJrWSRTCJHNjH5XWbBgAUuWLOGbb77BxsaGsWPHsmfPHoYNG0Z0dLQpYhQi/9Jo1OHdnp6wezdMnQpW+Xsiu8iYBPZduAPAa7LcghDCBIxObsLCwmjYsCEA9vb2xMbGAtC3b1/WrFmTvdEJkR89egQnTz7e7tgRLl2CVq3MF1MO2hR0E61Owb90YSq4yyKZQojsZ3Ry4+npyb179wAoXbo0f/75JwChoaEysZ8Qz3PuHNStC23awLVrj8sdHc0XUw5SFIV1J1LntpEZiYUQpmF0ctOiRQu2bt0KwMCBAxk5ciStW7emV69eMv+NEBlRFFi6FOrVg5AQsLeH27fNHVWOO3XtPlfuPsLBxpKONfJ3vyIhhPkY3bi/ZMkSdDodAIMHD6ZIkSIcPnyYTp06MXjw4GwPUIg8LzYWhgyBn35St9u1g1Wr1OHeBUzqjMQdq3vhJItkCiFMxOh3FwsLCywsHlf49OzZk549ewJw8+ZNSpSQmUaF0Dt9Gnr1Uifns7SEzz6DMWPUZRUKmEeJKWz7O3WRTOlILIQwnWx5h42IiOCDDz6Q1cKFeNrSpWpiU7IkHDgAH31UIBMbUBfJjEvSUraoI3VKyyKZQgjTyfS77IMHD+jTpw/FihWjePHizJ8/H51Ox8SJEylXrhx//vkny5YtM2WsQuQ9M2fCyJFqDU6jRuaOxqxSm6Req1NSFskUQphUppulPv74Yw4ePEi/fv3YuXMnI0eOZOfOnSQkJPDbb7/RtGlTU8YpRN5w6hQsWABLlqjNUHZ2MGeOuaMyu8t3HnLy2n0sLTT08JNRUkII08p0zc327dtZvnw5s2bNYsuWLSiKQqVKlfjjjz8ksRFCUeB//4OGDWHZMvj6a3NHlKuk1to0q1QMdxdZJFMIYVqZrrm5desWvr6+AJQrVw47OzvefvttkwUmRJ5x/z4MHAibN6vbXbtC//5mDSk3Sdbq2HjqJiAzEgshckama250Oh3W1tb6bUtLSxwLyMRjQmTo+HHw81MTGxsbmD8fNm2CwtJhNtWBC3e4+zARN0cbWlRxN3c4QogCINM1N4qiEBAQgK2tLQAJCQkMHjw4TYKzadOm7I1QiNxq1Sq1xiYlBcqVg3XrwN/f3FHlOqlNUq/6lcDGqmCOFBNC5KxMJzf9+vUz2H7zzTezPRgh8pRatdRFLl99Ve1A7Opq7ohynTuxifzxTyQgTVJCiJyT6eRm+fLlpoxDiLwhMhLc/2taqVEDAgOhShV1dW+RxuagG6ToFGp5F6KSh7O5wxFCFBBSRyxEZuh08MUXUKYM/PXX43IfH0lsMqAoCutO3gCgl8xILITIQZLcCPE8d+5Ax44wfjzEx8OGDeaOKE8IDHvApciH2Flb8EoNL3OHI4QoQGTlOiGe5cAB6N0bwsPVCfm++QYGDDB3VHnC+v86Eneo7oWznfVz9hZCiOwjNTdCpEerhWnToEULNbHx8VGHfQ8cKM1QmRCXlMLWM7cA6CkdiYUQOUySGyHSs3EjTJyo9rXp1w9OnIDq1c0dVZ6x42wEj5K0lHFzoH7ZIuYORwhRwEizlBDpee01+OUXaNtWTW6EUdadSF0k01sWyRRC5DipuREC1GaouXMhNlbd1mhg9WpJbLLgyp2HHL8ahYVGnbhPCCFymiQ3Qty6BS1bwqhRMGSIuaPJ8zacUod/N6lUDC9XezNHI4QoiCS5EQXbrl3qTMMHDoCTE3ToYO6I8rQUrU6f3PSSjsRCCDOR5EYUTCkp6rw17dqp89jUrAmnTsEbb5g7sjzt4L93iIxNpIijDS19PMwdjhCigJIOxaLguXkTevWCI0fU7ffeg9mz1XlsxAtZd0KttelaSxbJFEKYjyQ3ouCxtIRLl8DFBb7/Xh0ZJV7YvYeJ7A25DchyC0II85LkRhQMWq2a1AB4esKmTeDhAeXLmzeufGRz0E1SdAo1S7pS2VMWyRRCmI/UG4v87+pVaNQI1q59XNawoSQ22UhRFNY+MbeNEEKYkyQ3In/75ReoXVtdyXvsWEhKMndE+dKZG9H8G/kQWysLOtUsbu5whBAFnCQ3In9KSoIRI6BbN3jwAOrVU4d729iYO7J8RatTOHb5Hl/t/AeA9tU8cbWXRTKFEOYlfW5E/nPlijoa6uRJdfvDD+HzzyWxyWY7z4UzZWsw4dEJ+rKD/95l57lw2lXzMmNkQoiCTmpuRP4SGQl+fmpiU6QIbNkCs2ZJYpPNdp4LZ8iPgQaJDcD9R0kM+TGQnefCzRSZEEJIciPyG3d3GDhQ7TB8+jR06mTuiPIdrU5hytZglHSeSy2bsjUYrS69PYQQwvSkWUrkff/+C7a2UKqUuv3FF+q/1tL3wxSOh0alqbF5kgKERydwPDSKBuXdci4wIYT4j9lrbhYsWEDZsmWxs7PD39+fQ4cOZbjvpk2baN26NcWKFcPFxYUGDRqwa9euHIxW5Dpr1qjNUL17Q3KyWmZtLYmNCUXGZpzYZGU/IYTIbmZNbtauXcuIESP45JNPCAoKonHjxrRv356wsLB09z948CCtW7dmx44dnDp1iubNm9OpUyeCgoJyOHJhdvHx8M476lpQDx+qyUxsrLmjKhDcnTO3TEVm9xNCiOymURTFbA3j9evXx8/Pj4ULF+rLfHx86Nq1KzNmzMjUOapWrUqvXr2YOHFipvaPiYnB1dWV6OhoXFxcshS3MLN//lGXTDh3DjQa+PRTmDgRrKSVNSdodQqNvviDiJj0a2Y0gKerHYc/aoGlhSZngxNC5FvGfH6breYmKSmJU6dO0aZNG4PyNm3acPTo0UydQ6fTERsbS5EiRTLcJzExkZiYGIOHyMNWrQJ/fzWx8fCA3bth6lRJbHKQpYWGppWKpftcaiozqZOvJDZCCLMxW3Jz9+5dtFotHh4eBuUeHh5ERERk6hyzZ8/m0aNH9OzZM8N9ZsyYgaurq/7h7S1Tw+dZSUnq6t1xcdCypToaqlUrc0dV4DyIS2JXsPp/1MXOMKn0dLVj4Zt+Ms+NEMKszP51V6Mx/HanKEqasvSsWbOGyZMn8+uvv+Lu7p7hfuPHj2fUqFH67ZiYGElw8iobG1i3DjZuhI8+erwQpshR8/b+y4O4ZCp7OLNlaCMCwx4QGZuAu7Md9coWkRobIYTZmS25KVq0KJaWlmlqaSIjI9PU5jxt7dq1DBw4kPXr19PqOd/cbW1tsbW1feF4hRkoCixbBvfuqetCAVSuDB9/bN64CrBLkbH88Oc1ACZ28sXW2lKGewshch2zNUvZ2Njg7+/Pnj17DMr37NlDw4YNMzxuzZo1BAQEsHr1ajp27GjqMIW5xMZC377w9tswfjwEBpo7IgFM2xaCVqfQ2teDRhWKmjscIYRIl1mbpUaNGkXfvn2pU6cODRo0YMmSJYSFhTF48GBAbVK6efMmq1atAtTE5q233uLrr7/mpZde0tf62Nvb4+rqarbXIbLZmTPQsydcvKg2PU2fDrVqmTuqAm/fP5EcuHgHa0sNH3fwMXc4QgiRIbMmN7169eLevXtMnTqV8PBwqlWrxo4dOyhdujQA4eHhBnPeLF68mJSUFN5//33ef/99fXm/fv1YsWJFTocvspuiwJIlMHw4JCZCyZLqJH0vv2zuyAq8ZK2OaduDAejfqCxlizqaOSIhhMiYWee5MQeZ5yYX698fUpPUV15Rf3aT/hy5wdLDoUzbFoybow37xjTDxU5mgBZC5Kw8Mc+NEGm89JI6X82sWepq3pLY5ApRj5L4eu9FAEa3rSyJjRAi1zP7UHBRgCkK3L4Nnp7q9jvvQLNm6ogokWvM2XOBmIQUfLxc6FlHplEQQuR+UnMjzOP+fejeHRo0gAcP1DKNRhKbXOZCRCyr/1L7vcmsw0KIvEKSG5Hzjh9XV/LevBlu3oQjR8wdkUiHoihM2xaMToH21Tx5qZw0Ewoh8gZJbkTOURSYMwcaNYKrV6FcOTh6FGS+olxpb0gkhy/dxcbSgvHtZei3ECLvkD43ImdERUFAAGzdqm736AHffw8yP1GulJii5bP/hn4PbFyWUm4OZo5ICCEyT2puRM4YN05NbGxtYcECdY0oSWxyrZVHr3L1XhzFnG15v3kFc4cjhBBGkZobkTNmzIArV9Rh3jLbcK5292Ei//v9EgBj2lbGyVbeJoQQeYvU3AjTuHMH5s5V+9mAOmfN3r2S2OQBs3dfIDYxheolXOnhV9Lc4QghhNHkK5nIfgcPQu/ecOuW2vQ0YIC5IxKZdP5WND+fuA6oq35byNBvIUQeJDU3Ivtoteoil82bq4lNlSpQt665oxKZlDr0W1HglRpe1C1TxNwhCSFElkjNjcget2/Dm2+qTU8Ab70F334LTk7mjUtk2q7zEfx5JQpbKwvGta9i7nCEECLLJLkRL27/fnj9dTXBcXBQk5qAAHNHJYyQkKzlsx0hALzTpBwlC8vQbyFE3iXJjXhxKSkQGQlVq6pDvH19zR2RMNKyI6Fcj4rHw8WWwU3LmzscIYR4IZLciKxJSVFX8AZo1UpdSqF1a7XmRuQpkTEJfPuHOvT7o3ZVcJSh30KIPE46FAvj7doFPj5w+fLjsi5dJLHJo2buusCjJC01vQvRtVYJc4cjhBAvTJIbkXkpKfDxx9CuHVy6BFOnmjsi8YLO3ohmQ+ANQF31W4Z+CyHyA6l/Fplz44Y6d83hw+r24MHqIpgiz1IUhanbzqMo0LVWcfxKFTZ3SEIIkS0kuRHPt3079OsH9+6Bs7O64GXPnuaOSryg7WfDOXH1PnbWFoxtJ0O/hRD5hyQ34tm2bYNOndSf/fxg7VqoIAsp5nUJyVpm7PgHgMFNy1O8kL2ZIxJCiOwjyY14tjZtoF49qF8fZs5UV/UWed53B69w80E8xV3teLeJDP0WQuQvktyItPbtg5dfBmtrsLGBAwfAzs7cUYlsEhGdwIL96ki3j9pXwd7G0swRCSFE9pLRUuKxpCQYMQJatIBJkx6XS2KTr3y18x/ik7X4ly5M55rFzR2OEEJkO6m5EaorV6BXLzh5Ut1OTgZFAY0MDc5PgsLusynoJgATX/FFI79fIUQ+JMmNgA0bYOBAiImBwoVh5crHnYhFvqEO/Q4GoLtfSWp6FzJvQEIIYSKS3BRkCQnw4YewYIG63aAB/PwzlCpl3rhEttHqFI6HRhEZm8C/t2MJCnuAg40lY9tVNndoQghhMpLcFGTXr6u1NAAffQTTpqmdiEW+sPNcOFO2BhMenWBQ3srHHQ8X6UclhMi/JLkpyCpWhGXL1In52rc3dzQiG+08F86QHwNR0nlu65lwOlQPp101rxyPSwghcoKMlipI4uPVZRMOHnxc1rOnJDb5jFanMGVrcLqJTaopW4PR6p61hxBC5F2S3BQU//yjTsS3eDH06aP2txH50vHQqDRNUU9SgPDoBI6HRuVcUEIIkYMkuSkIVq0Cf384exbc3dWmKJm7Jt+KjM1c4prZ/YQQIq+R5CY/e/QI+vdXF72Mi1Mn5zt9Glq3NndkwoTcnTOXuGZ2PyGEyGukQ3F+FRUFjRtDcDBYWKgzDn/yCVjKVPv5nZuTDZYaDVol/T41GsDT1Y56ZYvkbGBCCJFDJLnJrwoXhqpV4f59WL0amjUzd0QiB/x15R7v/HDqmYkNwKROvlhayOzEQoj8SZKb/OThQ9BqwdVVXTbhu+8gMVHtZyPyvV9P32TM+r9J0uqo5V2IN+qXYu6eiwadiz1d7ZjUyVeGgQsh8jVJbvKLM2fUYd3Vq8P69Wpy4+pq7qhEDlAUhW/3XWLW7osAtKvqybzXa2FnbUl3v5L6GYrdndWmKKmxEULkd5Lc5HWKAkuWwPDhai3No0cQHg7FZbXngiBZq+OTzWdZd/IGAIMal2V8ex8s/ktgLC00NCjvZs4QhRAix0lyk5fFxMA778Datep2x46wYgUULWrWsETOiElI5v2fAjn0710sNDClc1X6Nihj7rCEEMLsJLnJqwID1Waoy5fBygpmzIBRo9SRUSLfu/kgngHLT3DhdiwONpZ880ZtWlTxMHdYQgiRK0hykxelpDxObEqVUmtuXnrJ3FGJHHLuZjT9V5zgTmwi7s62LAuoS7US0r9KCCFSydf8vMjKSm1+6t4dgoIksSlAfg+5Tc/Fx7gTm0hlD2d+eb+RJDZCCPEUqbnJK06cgGvXoEcPdfvll9WHKDB+OHaVSVvOo1OgccWifNvHDxc7a3OHJYQQuY4kN7mdosC8efDRR2BtDb6+6kMUGDqdwuc7Qvj+cCgAr9f1ZlrXalhbSsWrEEKkR5Kb3CwqSl0bassWdbtzZxniXcDEJ2kZufY0O89HADCmbWXea1YejUbmqhFCiIxIcpNbHT0Kr78O16+DjQ3MnQtDhqiT84kC4e7DRN5eeZLT1x9gY2nBzNdq0KVWCXOHJYQQuZ4kN7nRrFkwbpy6lEKFCrBuHdSube6oRA66FPmQ/iuOcz0qnkIO1izpW0cWuhRCiEyS5CY3evBATWxefx0WLwYXF3NHJExEq1PSLI9w4moU76w6SUxCCqXdHFgeUJdyxZzMHaoQQuQZktzkFikp6hBvgMmTwd8funaVZqh8bOe5cKZsDTZY2NLV3pqHiclodeBXqhDfvVUHNydbM0YphBB5jwy3MDedDj77TB3WnZiolllZQbduktjkYzvPhTPkx0CDxAYgOv5xYrN60EuS2AghRBZIcmNOt29Du3bw6afw11/qat4i39PqFKZsDUZ5xj7h0Qky1FsIIbJI3j3N5Y8/oFYt2LMH7O1h2TLo08fcUYkccDw0Kk2NzdPCoxM4HhqVQxEJIUT+In1ucppWC9OmwdSp6gR9vr5qjY1MzJfv3bgfx+8hkaw+Hpap/SNjn50ACSGESJ8kNzlt1CiYP1/9ecAA+N//wMHBvDEJk9DpFP6+Gc3vIbfZE3ybfyJijTre3dnORJEJIUT+JslNThs+HDZuhC++gDffNHc0IhPSG65taZF+Z+/4JC1HLt1lb8htfv8nkjuxifrnLDRQp0wRWlQpxtJDodx9mJRuvxsN4OlqJ/PaCCFEFklyY2opKbBvH7RurW6XKweXL4OtjILJC9Ibru3lasekTr60q+YFqM1Hf4REsjfkNocv3SUhWaff18nWiqaVitHSx53mld0p7GgDQBk3R4b8GIgGDBKc1JRpUiffDBMoIYQQz6ZRFOVZgzbynZiYGFxdXYmOjsYlGyfHS/fb/a2b8MYbcPgw7NwJbdpk7/mz6cPPlOfOy+dPHa799H+Q1ISkc00vrkXFc+b6A4PnSxSyp5WPO618Pahf1g0bq/T77WcmcRJCCKEy5vPb7DU3CxYsYObMmYSHh1O1alXmzZtH48aNM9z/wIEDjBo1ivPnz1O8eHHGjh3L4MGDczDitNL7kHo14gxf/DoLmwf3wdkZHj3K1vNn14egqT9g8+r5nzVcO7Vsy5lwfVlN70K0qqImNFU8nTO1sGW7al609vU0aeInhBAFkVlrbtauXUvfvn1ZsGABjRo1YvHixXz//fcEBwdTqlSpNPuHhoZSrVo1Bg0axLvvvsuRI0d47733WLNmDd27d8/UNbO75ubpb/dW2hRGH1zF4OObAIj2qY7rlk3qGlHZcP5UqR9/C9/0y/KHuCnPnVvPrygKsYkpRMclEx2fzIP//o2OT+ZBfJL6c1wyV+485PjV+8+NYVDjsgxqXA53F+n8K4QQpmTM57dZk5v69evj5+fHwoUL9WU+Pj507dqVGTNmpNn/o48+YsuWLYSEhOjLBg8ezJkzZzh27FimrpmdyY1Wp/Dyl3/oaw1KREfyvy1f4nfrAgDL/TvxfafBrB7aNEvfxrU6hR6Ljhl0Sn2au7Mt6wc3MPr8mT33uneNP3fq+V9b/OzzF3OyZcWAumjQoFOU/x6gUxSU1J91j8uefF6r1TFmw9/cj0vO8Px21hbULVOEmIQUouOS9EmMLhv/4r9+vZas1C2EEDkgTzRLJSUlcerUKcaNG2dQ3qZNG44ePZruMceOHaPNU/1W2rZty9KlS0lOTsba2jrNMYmJiSQmPv6AjYmJyYboVU9Pxlbv+jn8bl0gxtaRMe2Hs6tyQ4hXaDpzf7Zd82mRsYkmO39kbCLNZpnm3AB3HibScf5hk50/IVnHoX/vpvucnbUFrvbWuNpbU8jeBhd7awo5pG5bExWXxPIjV597DRmuLYQQuY/Zkpu7d++i1Wrx8PAwKPfw8CAiIiLdYyIiItLdPyUlhbt37+LllbaJY8aMGUyZMiX7An/C05Osba7WAq/Yu2zxacKNQp76cisLTZZrP1IyUc2QlfNn9tzWlobn1vDs66R2NUnR6kjSPv/8zrZW2NtYYqHRYKEBjUa9noUGLDQaNP/9a6HRYPFEeXR8EmFR8c89f5/6pWhW2d0gcXGxt8bO2vKZx2l1CjvPRRARnSDDtYUQIo8xe4fipzteKoryzM6Y6e2fXnmq8ePHM2rUKP12TEwM3t7eWQ3XQHrf2hc06Jmm7IeB9WlQ3s3o8x+7fI/e3/353P2ycv7MnnvVANPGvuStOiY9/ys1imfp/JYWGiZ18pXh2kIIkQeZbW2pokWLYmlpmaaWJjIyMk3tTCpPT89097eyssLNLf0PMFtbW1xcXAwe2aVe2SJ4udplWJehQR25k9Vv96Y8f16OPSfOD+popoVv+uHpapjEerravXBnaCGEEKZjtuTGxsYGf39/9uzZY1C+Z88eGjZsmO4xDRo0SLP/7t27qVOnTrr9bUwt9ds9kOZDNju+3Zvy/Hk59pw4f6p21bw4/FEL1gx6ia9fr8WaQS9x+KMWktgIIUQuZtZVwUeNGsX333/PsmXLCAkJYeTIkYSFhennrRk/fjxvvfWWfv/Bgwdz7do1Ro0aRUhICMuWLWPp0qWMHj3aXC/B5N/uTXn+vBx7Tpw/laWFhgbl3ehSqwQNyrtJU5QQQuRyZp+heMGCBXz11VeEh4dTrVo15s6dS5MmTQAICAjg6tWr7N+/X7//gQMHGDlypH4Sv48++sioSfxydIbiPDALr6nPnR/OL4QQwvzyzDw35mCq5EYIIYQQpmPM57dZm6WEEEIIIbKbJDdCCCGEyFckuRFCCCFEviLJjRBCCCHyFUluhBBCCJGvSHIjhBBCiHxFkhshhBBC5CuS3AghhBAiX5HkRgghhBD5ipW5A8hpqRMyx8TEmDkSIYQQQmRW6ud2ZhZWKHDJTWxsLADe3t5mjkQIIYQQxoqNjcXV1fWZ+xS4taV0Oh23bt3C2dkZjSZ7F1eMiYnB29ub69evy7pVJiT3OWfIfc4Zcp9zjtzrnGGq+6woCrGxsRQvXhwLi2f3qilwNTcWFhaULFnSpNdwcXGR/zg5QO5zzpD7nDPkPuccudc5wxT3+Xk1NqmkQ7EQQggh8hVJboQQQgiRr0hyk41sbW2ZNGkStra25g4lX5P7nDPkPucMuc85R+51zsgN97nAdSgWQgghRP4mNTdCCCGEyFckuRFCCCFEviLJjRBCCCHyFUluhBBCCJGvSHJjpAULFlC2bFns7Ozw9/fn0KFDz9z/wIED+Pv7Y2dnR7ly5Vi0aFEORZq3GXOfN23aROvWrSlWrBguLi40aNCAXbt25WC0eZexf8+pjhw5gpWVFbVq1TJtgPmEsfc5MTGRTz75hNKlS2Nra0v58uVZtmxZDkWbdxl7n3/66Sdq1qyJg4MDXl5e9O/fn3v37uVQtHnTwYMH6dSpE8WLF0ej0fDLL7889xizfA4qItN+/vlnxdraWvnuu++U4OBgZfjw4Yqjo6Ny7dq1dPe/cuWK4uDgoAwfPlwJDg5WvvvuO8Xa2lrZsGFDDkeetxh7n4cPH658+eWXyvHjx5WLFy8q48ePV6ytrZXAwMAcjjxvMfY+p3rw4IFSrlw5pU2bNkrNmjVzJtg8LCv3uXPnzkr9+vWVPXv2KKGhocpff/2lHDlyJAejznuMvc+HDh1SLCwslK+//lq5cuWKcujQIaVq1apK165dczjyvGXHjh3KJ598omzcuFEBlM2bNz9zf3N9DkpyY4R69eopgwcPNiirUqWKMm7cuHT3Hzt2rFKlShWDsnfffVd56aWXTBZjfmDsfU6Pr6+vMmXKlOwOLV/J6n3u1auX8umnnyqTJk2S5CYTjL3Pv/32m+Lq6qrcu3cvJ8LLN4y9zzNnzlTKlStnUDZ//nylZMmSJosxv8lMcmOuz0FplsqkpKQkTp06RZs2bQzK27Rpw9GjR9M95tixY2n2b9u2LSdPniQ5OdlkseZlWbnPT9PpdMTGxlKkSBFThJgvZPU+L1++nMuXLzNp0iRTh5gvZOU+b9myhTp16vDVV19RokQJKlWqxOjRo4mPj8+JkPOkrNznhg0bcuPGDXbs2IGiKNy+fZsNGzbQsWPHnAi5wDDX52CBWzgzq+7evYtWq8XDw8Og3MPDg4iIiHSPiYiISHf/lJQU7t69i5eXl8nizauycp+fNnv2bB49ekTPnj1NEWK+kJX7/O+//zJu3DgOHTqElZW8dWRGVu7zlStXOHz4MHZ2dmzevJm7d+/y3nvvERUVJf1uMpCV+9ywYUN++uknevXqRUJCAikpKXTu3Jn//e9/ORFygWGuz0GpuTGSRqMx2FYUJU3Z8/ZPr1wYMvY+p1qzZg2TJ09m7dq1uLu7myq8fCOz91mr1fLGG28wZcoUKlWqlFPh5RvG/D3rdDo0Gg0//fQT9erVo0OHDsyZM4cVK1ZI7c1zGHOfg4ODGTZsGBMnTuTUqVPs3LmT0NBQBg8enBOhFijm+ByUr1+ZVLRoUSwtLdN8C4iMjEyTlaby9PRMd38rKyvc3NxMFmtelpX7nGrt2rUMHDiQ9evX06pVK1OGmecZe59jY2M5efIkQUFBDB06FFA/hBVFwcrKit27d9OiRYsciT0vycrfs5eXFyVKlMDV1VVf5uPjg6Io3Lhxg4oVK5o05rwoK/d5xowZNGrUiDFjxgBQo0YNHB0dady4MdOnT5ea9Wxirs9BqbnJJBsbG/z9/dmzZ49B+Z49e2jYsGG6xzRo0CDN/rt376ZOnTpYW1ubLNa8LCv3GdQam4CAAFavXi1t5plg7H12cXHh7NmznD59Wv8YPHgwlStX5vTp09SvXz+nQs9TsvL33KhRI27dusXDhw/1ZRcvXsTCwoKSJUuaNN68Kiv3OS4uDgsLw49AS0tL4HHNgnhxZvscNGl35Xwmdajh0qVLleDgYGXEiBGKo6OjcvXqVUVRFGXcuHFK37599funDoEbOXKkEhwcrCxdulSGgmeCsfd59erVipWVlfLtt98q4eHh+seDBw/M9RLyBGPv89NktFTmGHufY2NjlZIlSyo9evRQzp8/rxw4cECpWLGi8vbbb5vrJeQJxt7n5cuXK1ZWVsqCBQuUy5cvK4cPH1bq1Kmj1KtXz1wvIU+IjY1VgoKClKCgIAVQ5syZowQFBemH3OeWz0FJboz07bffKqVLl1ZsbGwUPz8/5cCBA/rn+vXrpzRt2tRg//379yu1a9dWbGxslDJlyigLFy7M4YjzJmPuc9OmTRUgzaNfv345H3geY+zf85Mkuck8Y+9zSEiI0qpVK8Xe3l4pWbKkMmrUKCUuLi6Ho857jL3P8+fPV3x9fRV7e3vFy8tL6dOnj3Ljxo0cjjpv2bdv3zPfb3PL56BGUaT+TQghhBD5h/S5EUIIIUS+IsmNEEIIIfIVSW6EEEIIka9IciOEEEKIfEWSGyGEEELkK5LcCCGEECJfkeRGCCGEEPmKJDdCCCGEyFckuRHChJo1a8aIESNe6BxXr15Fo9Fw+vRpAPbv349Go+HBgwdZOj49xp4zszQaDb/88ku2nvN5AgIC6Nq1a45eUwiRu8iq4EKY0KZNm7J9cbiGDRsSHh5usGr0s3h7exMeHk7RokWzNQ5zu3r1KmXLliUoKIhatWrpy7/++uscWfgwICCABw8eZFvy1qxZMwICAggICMiW8wlRkElyI4QJFSlSJNvPaWNjg6enZ6b3t7S0NGr/vC6zSV9ukZSUhI2NTY5fNzk5OU3inV5ZVs8lhDlJs5QQJvR0s1SZMmX4/PPPGTBgAM7OzpQqVYolS5YYHHP8+HFq166NnZ0dderUISgoyOD5J5uQoqOjsbe3Z+fOnQb7bNq0CUdHRx4+fJhus9SOHTuoVKkS9vb2NG/enKtXrxocP3nyZIPaEIB58+ZRpkwZ/faJEydo3bo1RYsWxdXVlaZNmxIYGGjU/VEUha+++opy5cphb29PzZo12bBhg/75+/fv06dPH4oVK4a9vT0VK1Zk+fLlAJQtWxaA2rVro9FoaNasGZC2WapZs2Z88MEHjBgxgsKFC+Ph4cGSJUt49OgR/fv3x9nZmfLly/Pbb7/pj9FqtQwcOJCyZctib29P5cqV+frrrw3uz8qVK/n111/RaDRoNBr2798PwNmzZ2nRogX29va4ubnxzjvv8PDhQ/2xqfHNmDGD4sWLU6lSpXTvzeTJkylVqhS2trYUL16cYcOGPfNebt26FX9/f+zs7ChXrhxTpkwhJSVF/7xGo2HRokV06dIFR0dHpk+frv89L1u2jHLlymFra4uiKISFhdGlSxecnJxwcXGhZ8+e3L592yC29I4TIreQ5EaIHDZ79mx90vLee+8xZMgQ/vnnHwAePXrEK6+8QuXKlTl16hSTJ09m9OjRGZ7L1dWVjh078tNPPxmUr169Wv/h9LTr16/z6quv0qFDB06fPs3bb7/NuHHjjH4dsbGx9OvXj0OHDvHnn39SsWJFOnToQGxsbKbP8emnn7J8+XIWLlzI+fPnGTlyJG+++SYHDhwAYMKECQQHB/Pbb78REhLCwoUL9c1rx48fB2Dv3r2Eh4ezadOmDK+zcuVKihYtyvHjx/nggw8YMmQIr732Gg0bNiQwMJC2bdvSt29f4uLiANDpdJQsWZJ169YRHBzMxIkT+fjjj1m3bh0Ao0ePpmfPnrRr147w8HDCw8Np2LAhcXFxtGvXjsKFC3PixAnWr1/P3r17GTp0qEE8v//+OyEhIezZs4dt27aliXfDhg3MnTuXxYsX8++///LLL79QvXr1DF/frl27ePPNNxk2bBjBwcEsXryYFStW8NlnnxnsN2nSJLp06cLZs2cZMGAAAJcuXWLdunVs3LhRnwB37dqVqKgoDhw4wJ49e7h8+TK9evUyOFd6xwmRa5h83XEhCrCmTZsqw4cP12+XLl1aefPNN/XbOp1OcXd3VxYuXKgoiqIsXrxYKVKkiPLo0SP9PgsXLlQAJSgoSFEURdm3b58CKPfv31cURVE2bdqkODk56Y+Jjo5W7OzslO3btyuKoiihoaEGx48fP17x8fFRdDqd/hofffSRwTknTZqk1KxZ0+C1zJ07VyldunSGrzUlJUVxdnZWtm7dqi8DlM2bN6e7/8OHDxU7Ozvl6NGjBuUDBw5UevfurSiKonTq1Enp379/usc//bpS9evXT+nSpYt+u2nTpsrLL79sEKejo6PSt29ffVl4eLgCKMeOHcvw9b333ntK9+7dM7yOoijKkiVLlMKFCysPHz7Ul23fvl2xsLBQIiIi9Md5eHgoiYmJGV5r9uzZSqVKlZSkpKQM93lS48aNlc8//9yg7IcfflC8vLz024AyYsQIg30mTZqkWFtbK5GRkfqy3bt3K5aWlkpYWJi+7Pz58wqgHD9+PMPjhMhNpOZGiBxWo0YN/c8ajQZPT08iIyMBCAkJoWbNmjg4OOj3adCgwTPP17FjR6ysrNiyZQsAGzduxNnZmTZt2qS7f0hICC+99BIajSbT10hPZGQkgwcPplKlSri6uuLq6srDhw8JCwvL1PHBwcEkJCTQunVrnJyc9I9Vq1Zx+fJlAIYMGcLPP/9MrVq1GDt2LEePHjU6TjC855aWlri5uRnUhHh4eOhfU6pFixZRp04dihUrhpOTE999991zX1vq78/R0VFf1qhRI3Q6HRcuXNCXVa9e/Zn9bF577TXi4+MpV64cgwYNYvPmzQZNTE87deoUU6dONbiPgwYNIjw8XF8bBVCnTp00x5YuXZpixYoZvAZvb2+8vb31Zb6+vhQqVIiQkJAMjxMiN5EOxULksKc7Xmo0GnQ6HUCW+i3Y2NjQo0cPVq9ezeuvv87q1avp1asXVlbp//fOzDUsLCzS7JecnGywHRAQwJ07d5g3bx6lS5fG1taWBg0akJSUlKm4U1/z9u3bKVGihMFztra2ALRv355r166xfft29u7dS8uWLXn//feZNWtWpq6RKr17/mRZaqKXGtO6desYOXIks2fPpkGDBjg7OzNz5kz++uuvZ15HURSDpPHpa6Z6MvlJj7e3NxcuXGDPnj3s3buX9957j5kzZ3LgwIF0O+7qdDqmTJnCq6++muY5Ozu7Z1736bKMXsPT5c97DUKYkyQ3QuQivr6+/PDDD8THx2Nvbw/An3/++dzj+vTpQ5s2bTh//jz79u1j2rRpz7zG08OXn75GsWLFiIiIMPhAe7pfxaFDh1iwYAEdOnQA1L48d+/efW6sT8Zha2tLWFgYTZs2zXC/YsWK6YdIN27cmDFjxjBr1ix9zYdWq830NTPr0KFDNGzYkPfee09fllqblMrGxibNtX19fVm5ciWPHj3Sf/gfOXIECwuLDDsOZ8Te3p7OnTvTuXNn3n//fapUqcLZs2fx8/NLs6+fnx8XLlygQoUKRl0jPb6+voSFhXH9+nV97U1wcDDR0dH4+Pi88PmFyAnSLCVELvLGG29gYWHBwIEDCQ4OZseOHZmqpWjatCkeHh706dOHMmXK8NJLL2W47+DBg7l8+TKjRo3iwoULrF69mhUrVhjs06xZM+7cucNXX33F5cuX+fbbbw1GEwFUqFCBH374gZCQEP766y/69OmjT8gyw9nZmdGjRzNy5EhWrlzJ5cuXCQoK4ttvv2XlypUATJw4kV9//ZVLly5x/vx5tm3bpv+AdXd3148Uu337NtHR0Zm+9vNUqFCBkydPsmvXLi5evMiECRM4ceKEwT5lypTh77//5sKFC9y9e5fk5GT69OmDnZ0d/fr149y5c+zbt48PPviAvn376pu+MmPFihUsXbqUc+fOceXKFX744Qfs7e0pXbp0uvtPnDiRVatWMXnyZM6fP09ISAhr167l008/Nfq1t2rViho1atCnTx8CAwM5fvw4b731Fk2bNk23WUuI3EiSGyFyEScnJ7Zu3UpwcDC1a9fmk08+4csvv3zucRqNht69e3PmzBn69OnzzH1LlSrFxo0b2bp1KzVr1mTRokV8/vnnBvv4+PiwYMECvv32W2rWrMnx48fTjNpatmwZ9+/fp3bt2vTt25dhw4bh7u5u1OudNm0aEydOZMaMGfj4+NC2bVu2bt2qH+ZtY2PD+PHjqVGjBk2aNMHS0pKff/4ZACsrK+bPn8/ixYspXrw4Xbp0MerazzJ48GBeffVVevXqRf369bl3755BLQ7AoEGDqFy5sr5fzpEjR3BwcGDXrl1ERUVRt25devToQcuWLfnmm2+Mun6hQoX47rvvaNSoETVq1OD3339n69atuLm5pbt/27Zt2bZtG3v27KFu3bq89NJLzJkzJ8Nk6FlSZ5UuXLgwTZo0oVWrVpQrV461a9cafS4hzEWjZKWRXwghhBAil5KaGyGEEELkK5LcCCGEECJfkeRGCCGEEPmKJDdCCCGEyFckuRFCCCFEviLJjRBCCCHyFUluhBBCCJGvSHIjhBBCiHxFkhshhBBC5CuS3AghhBAiX5HkRgghhBD5yv8BqHsI5VDod7YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "x = np.linspace(0,1,20)\n",
    "y = []\n",
    "# epsilon：单颗树的误差率\n",
    "for epsilon in np.linspace(0,1,20):\n",
    "    E = np.array([comb(25,i)*(epsilon**i)*((1-epsilon)**(25-i)) for i in range(13,26)]).sum()      \n",
    "    y.append(E)\n",
    "plt.plot(x,y,\"o-\",label=\"when estimators are different\")\n",
    "plt.plot(x,x,\"--\",color=\"red\",label=\"if all estimators are same\")\n",
    "plt.xlabel(\"individual estimator's error\")\n",
    "plt.ylabel(\"RandomForest's error\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 随机森林-回归"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "class sklearn.ensemble.RandomForestRegressor (n_estimators=’warn’, criterion=’mse’, max_depth=None,\n",
    "min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’,\n",
    "max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, oob_score=False,\n",
    "n_jobs=None, random_state=None, verbose=0, warm_start=False)\\\n",
    "所有的参数，属性与接口，全部和随机森林分类器一致。仅有的不同就是回归树与分类树的不同，不纯度指标，优化准则Criterion。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\torch1.8\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-11.22504076,  -5.3945749 ,  -4.74755867, -22.54699078,\n",
       "       -12.31243335, -17.18030718,  -6.94019868, -94.14567212,\n",
       "       -28.541145  , -14.6250416 ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_boston # 标签是连续型变量\n",
    "from sklearn.model_selection import cross_val_score \n",
    "from sklearn.ensemble import RandomForestRegressor \n",
    "\n",
    "boston = load_boston()\n",
    "regressor = RandomForestRegressor(n_estimators=100,random_state=0)\n",
    "\n",
    "scores = cross_val_score(regressor, boston.data, boston.target, cv=10\n",
    "               ,scoring = \"neg_mean_squared_error\" # scoring：默认是R平方\n",
    "               )\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['accuracy',\n",
       " 'adjusted_mutual_info_score',\n",
       " 'adjusted_rand_score',\n",
       " 'average_precision',\n",
       " 'balanced_accuracy',\n",
       " 'brier_score_loss',\n",
       " 'completeness_score',\n",
       " 'explained_variance',\n",
       " 'f1',\n",
       " 'f1_macro',\n",
       " 'f1_micro',\n",
       " 'f1_samples',\n",
       " 'f1_weighted',\n",
       " 'fowlkes_mallows_score',\n",
       " 'homogeneity_score',\n",
       " 'mutual_info_score',\n",
       " 'neg_log_loss',\n",
       " 'neg_mean_absolute_error',\n",
       " 'neg_mean_squared_error',\n",
       " 'neg_mean_squared_log_error',\n",
       " 'neg_median_absolute_error',\n",
       " 'normalized_mutual_info_score',\n",
       " 'precision',\n",
       " 'precision_macro',\n",
       " 'precision_micro',\n",
       " 'precision_samples',\n",
       " 'precision_weighted',\n",
       " 'r2',\n",
       " 'recall',\n",
       " 'recall_macro',\n",
       " 'recall_micro',\n",
       " 'recall_samples',\n",
       " 'recall_weighted',\n",
       " 'roc_auc',\n",
       " 'v_measure_score']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sklearn当中的模型评估指标列表\n",
    "import sklearn\n",
    "sorted(sklearn.metrics.SCORERS.keys()) # 这些是sklearn提供的scoring可选参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用RF完成缺失值填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.impute import SimpleImputer # 填补缺失值的类\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\torch1.8\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "dataset = load_boston()\n",
    "print(dataset.data.shape)\n",
    "# 总共506*13=6578个数据\n",
    " \n",
    "X_full, y_full = dataset.data, dataset.target\n",
    "n_samples = X_full.shape[0] # 506\n",
    "n_features = X_full.shape[1] # 13\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将数据集中的部分数据设置为缺失值\n",
    "# 首先确定缺失值比例50%，那总共就要有3289个数据缺失\n",
    " \n",
    "rng = np.random.RandomState(0)#设置一个随机种子，方便观察\n",
    "missing_rate = 0.5\n",
    "\n",
    "# np.floor向下取整，返回.0格式的浮点数\n",
    "n_missing_samples = int(np.floor(n_samples * n_features * missing_rate))\n",
    "\n",
    "# 通过随机行索引和列索引来为3289个位置赋空值\n",
    "# 然后分别用0，均值和随机森林来填充这些缺失值，然后查看回归的结果如何\n",
    "missing_features = rng.randint(0,n_features,n_missing_samples)\n",
    "len(missing_features) # 3289\n",
    "missing_samples = rng.randint(0,n_samples,n_missing_samples)\n",
    "len(missing_samples) # 3289\n",
    "\n",
    "X_missing = X_full.copy()\n",
    "y_missing = y_full.copy()\n",
    "\n",
    "X_missing[missing_samples,missing_features] = np.nan\n",
    "#并没有对y_missing进行缺失值填补，原因是有监督学习，不能缺标签啊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(506, 13)\n"
     ]
    }
   ],
   "source": [
    "# 使用均值进行填补\n",
    "\n",
    "# 实例化SimpleImputer对象\n",
    "imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean') \n",
    "# fit_transform() 默认返回的是新数据\n",
    "X_missing_mean = imp_mean.fit_transform(X_missing)\n",
    "print(X_missing_mean.shape)\n",
    "\n",
    "#使用指定值进行填补\n",
    "imp_0 = SimpleImputer(missing_values=np.nan, strategy=\"constant\", fill_value=0)\n",
    "X_missing_0 = imp_0.fit_transform(X_missing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用RF进行缺失值填充：\\\n",
    "a. 特征T存在缺失值，而其他特征完整：将不缺失特征T的样本和其对应的标签作为新的训练集，新标签是特征T的值；然后去预测每一个缺失特征T的样本；\\\n",
    "b. 多个特征都存在缺失值：从缺失值最少的特征开始填充，其他特征的缺失值暂时用0填充，然后按a进行填充；如此反复，直至不再有缺失值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.258666</td>\n",
       "      <td>18.00</td>\n",
       "      <td>7.2199</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.67828</td>\n",
       "      <td>65.20</td>\n",
       "      <td>4.090000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>296.00</td>\n",
       "      <td>17.989</td>\n",
       "      <td>391.6468</td>\n",
       "      <td>4.9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.027310</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.3912</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469000</td>\n",
       "      <td>6.16078</td>\n",
       "      <td>78.90</td>\n",
       "      <td>4.967100</td>\n",
       "      <td>2.00</td>\n",
       "      <td>296.53</td>\n",
       "      <td>18.277</td>\n",
       "      <td>396.9000</td>\n",
       "      <td>9.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.027290</td>\n",
       "      <td>11.27</td>\n",
       "      <td>7.0700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.467249</td>\n",
       "      <td>7.18500</td>\n",
       "      <td>61.10</td>\n",
       "      <td>4.278700</td>\n",
       "      <td>2.00</td>\n",
       "      <td>242.00</td>\n",
       "      <td>17.829</td>\n",
       "      <td>392.2324</td>\n",
       "      <td>4.9040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.078110</td>\n",
       "      <td>15.28</td>\n",
       "      <td>2.8367</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458000</td>\n",
       "      <td>6.73522</td>\n",
       "      <td>45.80</td>\n",
       "      <td>4.807887</td>\n",
       "      <td>3.66</td>\n",
       "      <td>222.00</td>\n",
       "      <td>18.700</td>\n",
       "      <td>393.7392</td>\n",
       "      <td>6.1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.079312</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.1800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.470398</td>\n",
       "      <td>7.14700</td>\n",
       "      <td>60.83</td>\n",
       "      <td>4.354979</td>\n",
       "      <td>3.84</td>\n",
       "      <td>245.13</td>\n",
       "      <td>18.700</td>\n",
       "      <td>394.1836</td>\n",
       "      <td>5.3300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0      1       2    3         4        5      6         7     8   \\\n",
       "0  0.258666  18.00  7.2199  0.1  0.538000  6.67828  65.20  4.090000  1.00   \n",
       "1  0.027310   0.00  5.3912  0.0  0.469000  6.16078  78.90  4.967100  2.00   \n",
       "2  0.027290  11.27  7.0700  0.0  0.467249  7.18500  61.10  4.278700  2.00   \n",
       "3  0.078110  15.28  2.8367  0.0  0.458000  6.73522  45.80  4.807887  3.66   \n",
       "4  0.079312   0.00  2.1800  0.0  0.470398  7.14700  60.83  4.354979  3.84   \n",
       "\n",
       "       9       10        11      12  \n",
       "0  296.00  17.989  391.6468  4.9800  \n",
       "1  296.53  18.277  396.9000  9.1400  \n",
       "2  242.00  17.829  392.2324  4.9040  \n",
       "3  222.00  18.700  393.7392  6.1406  \n",
       "4  245.13  18.700  394.1836  5.3300  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_missing_reg = pd.DataFrame(X_missing.copy()) \n",
    "\n",
    "# np.argsort 返回输入的一串索引，索引的排列 对应 其位置的值的升序\n",
    "sortindex = np.argsort(X_missing_reg.isnull().sum(axis=0))\n",
    "sortindex = sortindex.values\n",
    "\n",
    "# 按缺失值数量从少到多 依次填充特征\n",
    "for i in sortindex:\n",
    "\n",
    "    df = X_missing_reg\n",
    "    # 填充i位置的特征，所以特征i变成本次训练的标签\n",
    "    fillc = df.iloc[:,i]\n",
    "    \n",
    "    # 本次训练的特征矩阵 = 非标签特征+原标签\n",
    "    df = pd.concat([df.iloc[:, df.columns != i], pd.DataFrame(y_full)], axis=1)\n",
    "    \n",
    "    #在新特征矩阵中，对其他缺失值进行0填补\n",
    "    df_0 =SimpleImputer(missing_values=np.nan,strategy='constant',fill_value=0).fit_transform(df)\n",
    "                        \n",
    "    #找出训练集和测试集\n",
    "    # 注意 Ytrain，Ytest都是series，需要的是Ytrain，Ytest的行索引\n",
    "    Ytrain = fillc[fillc.notnull()] # Ytrain的值不是Nan，用来作为训练标签 \n",
    "    Ytest = fillc[fillc.isnull()] # Ytest的值是Nan，最终目的是预测它\n",
    "\n",
    "    # 通过索引取得对应的数据\n",
    "    Xtrain = df_0[Ytrain.index,:]\n",
    "    Xtest = df_0[Ytest.index,:]\n",
    "    \n",
    "    #用随机森林回归来填补缺失值\n",
    "    rfc = RandomForestRegressor(n_estimators=100)\n",
    "    rfc = rfc.fit(Xtrain, Ytrain) # 导入训练集进行训练\n",
    "    Ypredict = rfc.predict(Xtest) #\n",
    "    \n",
    "    #将预测得到的特征值放到原特征矩阵中\n",
    "    X_missing_reg.iloc[X_missing_reg.iloc[:,i].isnull(), i] = Ypredict\n",
    "\n",
    "#检验是否有空值\n",
    "X_missing_reg.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#对不同填充方式得到的数据进行建模\n",
    " \n",
    "X = [X_full,X_missing_mean,X_missing_0,X_missing_reg]\n",
    " \n",
    "mse = []\n",
    "std = []\n",
    "for x in X:\n",
    "    estimator = RandomForestRegressor(random_state=0, n_estimators=100)\n",
    "    scores = cross_val_score(estimator,x,y_full,scoring='neg_mean_squared_error', cv=5).mean()\n",
    "    mse.append(scores * -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Full data', 21.571667100368845),\n",
       " ('Zero Imputation', 40.848037216676374),\n",
       " ('Mean Imputation', 49.626793201980185),\n",
       " ('Regressor Imputation', 20.25330792987769)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*zip(['Full data','Zero Imputation','Mean Imputation','Regressor Imputation'],mse)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_labels = ['Full data',\n",
    "            'Zero Imputation',\n",
    "            'Mean Imputation',\n",
    "            'Regressor Imputation']\n",
    "colors = ['r', 'g', 'b', 'orange']\n",
    " \n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = plt.subplot(111)\n",
    "for i in np.arange(len(mse)):\n",
    "    # barh为横向条形图，alpha表示条的粗度\n",
    "    ax.barh(i, mse[i], color=colors[i], alpha=0.6, align='center')\n",
    "ax.set_title('Imputation Techniques with Boston Data')\n",
    "ax.set_xlim(left=np.min(mse) * 0.9, right=np.max(mse) * 1.1)\n",
    "# ax.set_yticks(np.arange(len(mse)))\n",
    "# ax.set_xlabel('MSE')\n",
    "# ax.set_yticklabels(x_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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